hyperactive.optimizers.hyper_optimizer.HyperOptimizer.search() - Code Metrics - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

HyperOptimizer.search() B
last analyzed 2025-06-23 18:24 UTC

↳ Parent: hyperactive.optimizers.hyper_optimizer

Complexity

Conditions

Size

Total Lines	68
Code Lines	52

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	5
eloc	52
nop	3
dl	0
loc	68
rs	8.1042
c	0
b	0
f	0

How to fix Long Method

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

import numpy as np

from .objective_function import ObjectiveFunction
from .hyper_gradient_conv import HyperGradientConv
from .optimizer_attributes import OptimizerAttributes
from .constraint import Constraint


class HyperOptimizer(OptimizerAttributes):
    def __init__(self, **opt_params):
        super().__init__()
        self.opt_params = opt_params

    def setup_search(

        self,
        objective_function,
        s_space,
        n_iter,
        initialize,
        constraints,
        pass_through,
        callbacks,
        catch,
        max_score,
        early_stopping,
        random_state,
        memory,
        memory_warm_start,
        verbosity,
    ):
        self.objective_function = objective_function
        self.s_space = s_space
        self.n_iter = n_iter

        self.initialize = initialize
        self.constraints = constraints
        self.pass_through = pass_through
        self.callbacks = callbacks
        self.catch = catch
        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
        self.verbosity = verbosity

        if "progress_bar" in self.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, nth_process):
        self.nth_process = nth_process

        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=nth_process,
            **self.opt_params,
        )

        self.conv = self.gfo_optimizer.conv

    def search(self, nth_process, p_bar):
        self._setup_process(nth_process)

        gfo_wrapper_model = ObjectiveFunction(
            objective_function=self.objective_function,
            optimizer=self.gfo_optimizer,
            callbacks=self.callbacks,
            catch=self.catch,
            nth_process=self.nth_process,
        )
        gfo_wrapper_model.pass_through = self.pass_through

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

        gfo_objective_function = gfo_wrapper_model(self.s_space())

        self.gfo_optimizer.init_search(
            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(nth_process)
                    + "] "
                    + str(self.objective_function.__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(gfo_wrapper_model.optimizer.score_best),
                    best_pos=str(gfo_wrapper_model.optimizer.pos_best),
                    best_iter=str(
                        gfo_wrapper_model.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		import numpy as np
6
7		from .objective_function import ObjectiveFunction
8		from .hyper_gradient_conv import HyperGradientConv
9		from .optimizer_attributes import OptimizerAttributes
10		from .constraint import Constraint
11
12
13		class HyperOptimizer(OptimizerAttributes):
14		def __init__(self, **opt_params):
15		super().__init__()
16		self.opt_params = opt_params
17
18	View Code Duplication	def setup_search(
		0 ignored issues – show Duplication introduced 2023-02-01 18:26 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
19		self,
20		objective_function,
21		s_space,
22		n_iter,
23		initialize,
24		constraints,
25		pass_through,
26		callbacks,
27		catch,
28		max_score,
29		early_stopping,
30		random_state,
31		memory,
32		memory_warm_start,
33		verbosity,
34		):
35		self.objective_function = objective_function
36		self.s_space = s_space
37		self.n_iter = n_iter
38
39		self.initialize = initialize
40		self.constraints = constraints
41		self.pass_through = pass_through
42		self.callbacks = callbacks
43		self.catch = catch
44		self.max_score = max_score
45		self.early_stopping = early_stopping
46		self.random_state = random_state
47		self.memory = memory
48		self.memory_warm_start = memory_warm_start
49		self.verbosity = verbosity
50
51		if "progress_bar" in self.verbosity:
52		self.verbosity = ["progress_bar"]
53		else:
54		self.verbosity = []
55
56		def convert_results2hyper(self):
57		self.eval_times = sum(self.gfo_optimizer.eval_times)
58		self.iter_times = sum(self.gfo_optimizer.iter_times)
59
60		if self.gfo_optimizer.best_para is not None:
61		value = self.hg_conv.para2value(self.gfo_optimizer.best_para)
62		position = self.hg_conv.position2value(value)
63		best_para = self.hg_conv.value2para(position)
64		self.best_para = best_para
65		else:
66		self.best_para = None
67
68		self.best_score = self.gfo_optimizer.best_score
69		self.positions = self.gfo_optimizer.search_data
70		self.search_data = self.hg_conv.positions2results(self.positions)
71
72		results_dd = self.gfo_optimizer.search_data.drop_duplicates(
73		subset=self.s_space.dim_keys, keep="first"
74		)
75		self.memory_values_df = results_dd[
76		self.s_space.dim_keys + ["score"]
77		].reset_index(drop=True)
78
79		def _setup_process(self, nth_process):
80		self.nth_process = nth_process
81
82		self.hg_conv = HyperGradientConv(self.s_space)
83
84		initialize = self.hg_conv.conv_initialize(self.initialize)
85		search_space_positions = self.s_space.positions
86
87		# conv warm start for smbo from values into positions
88		if "warm_start_smbo" in self.opt_params:
89		self.opt_params["warm_start_smbo"] = (
90		self.hg_conv.conv_memory_warm_start(
91		self.opt_params["warm_start_smbo"]
92		)
93		)
94
95		gfo_constraints = [
96		Constraint(constraint, self.s_space)
97		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=nth_process,
106		**self.opt_params,
107		)
108
109		self.conv = self.gfo_optimizer.conv
110
111		def search(self, nth_process, p_bar):
112		self._setup_process(nth_process)
113
114		gfo_wrapper_model = ObjectiveFunction(
115		objective_function=self.objective_function,
116		optimizer=self.gfo_optimizer,
117		callbacks=self.callbacks,
118		catch=self.catch,
119		nth_process=self.nth_process,
120		)
121		gfo_wrapper_model.pass_through = self.pass_through
122
123		memory_warm_start = self.hg_conv.conv_memory_warm_start(
124		self.memory_warm_start
125		)
126
127		gfo_objective_function = gfo_wrapper_model(self.s_space())
128
129		self.gfo_optimizer.init_search(
130		gfo_objective_function,
131		self.n_iter,
132		self.max_time,
133		self.max_score,
134		self.early_stopping,
135		self.memory,
136		memory_warm_start,
137		False,
138		)
139		for nth_iter in range(self.n_iter):
140		if p_bar:
141		p_bar.set_description(
142		"["
143		+ str(nth_process)
144		+ "] "
145		+ str(self.objective_function.__name__)
146		+ " ("
147		+ self.optimizer_class.name
148		+ ")",
149		)
150
151		self.gfo_optimizer.search_step(nth_iter)
152		if self.gfo_optimizer.stop.check():
153		break
154
155		if p_bar:
156		p_bar.set_postfix(
157		best_score=str(gfo_wrapper_model.optimizer.score_best),
158		best_pos=str(gfo_wrapper_model.optimizer.pos_best),
159		best_iter=str(
160		gfo_wrapper_model.optimizer.p_bar._best_since_iter
161		),
162		)
163
164		p_bar.update(1)
165		p_bar.refresh()
166
167		self.gfo_optimizer.finish_search()
168
169		self.convert_results2hyper()
170
171		self._add_result_attributes(
172		self.best_para,
173		self.best_score,
174		self.gfo_optimizer.p_bar._best_since_iter,
175		self.eval_times,
176		self.iter_times,
177		self.search_data,
178		self.gfo_optimizer.random_seed,
179		)
180

SimonBlanke / Hyperactive

HyperOptimizer.search() B last analyzed 2025-06-23 18:24 UTC

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HyperOptimizer.search() B
last analyzed 2025-06-23 18:24 UTC