hyperactive.hyperactive.Hyperactive._create_shared_memory() - Code Metrics - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Hyperactive._create_shared_memory() B
last analyzed 2025-06-23 18:24 UTC

↳ Parent: hyperactive.hyperactive

Complexity

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Total Lines	25
Code Lines	19

Duplication

Lines	0
Ratio	0 %

Importance

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Metric	Value
cc	7
eloc	19
nop	1
dl	0
loc	25
rs	8
c	0
b	0
f	0

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


import copy
import multiprocessing as mp
import pandas as pd

from typing import Union, List, Dict, Type

from .optimizers import RandomSearchOptimizer
from .run_search import run_search

from .results import Results
from .print_results import PrintResults
from .search_space import SearchSpace


class Hyperactive:
    """
    Initialize the Hyperactive class to manage optimization processes.

    Parameters:
    - verbosity: List of verbosity levels (default: ["progress_bar", "print_results", "print_times"])
    - distribution: String indicating the distribution method (default: "multiprocessing")
    - n_processes: Number of processes to run in parallel or "auto" to determine automatically (default: "auto")

    Methods:
    - add_search: Add a new optimization search process with specified parameters
    - run: Execute the optimization searches
    - best_para: Get the best parameters for a specific search
    - best_score: Get the best score for a specific search
    - search_data: Get the search data for a specific search
    """

    def __init__(
        self,
        verbosity: list = ["progress_bar", "print_results", "print_times"],
        distribution: str = "multiprocessing",
        n_processes: Union[str, int] = "auto",
    ):
        super().__init__()
        if verbosity is False:
            verbosity = []

        self.verbosity = verbosity
        self.distribution = distribution
        self.n_processes = n_processes

        self.opt_pros = {}

    def _create_shared_memory(self):
        _bundle_opt_processes = {}

        for opt_pros in self.opt_pros.values():
            if opt_pros.memory != "share":
                continue
            name = opt_pros.objective_function.__name__

            _bundle_opt_processes.setdefault(name, []).append(opt_pros)

        for opt_pros_l in _bundle_opt_processes.values():
            # Check if the lengths of the search spaces of all optimizers in the list are the same.
            if (
                len(set(len(opt_pros.s_space()) for opt_pros in opt_pros_l))
                == 1
            ):
                manager = mp.Manager()  # get new manager.dict
                shared_memory = manager.dict()
                for opt_pros in opt_pros_l:
                    opt_pros.memory = shared_memory
            else:
                for opt_pros in opt_pros_l:
                    opt_pros.memory = opt_pros_l[
                        0
                    ].memory  # get same manager.dict

    @staticmethod
    def _default_opt(optimizer):
        if isinstance(optimizer, str):
            if optimizer == "default":
                optimizer = RandomSearchOptimizer()
        return copy.deepcopy(optimizer)

    @staticmethod
    def _default_search_id(search_id, objective_function):
        if not search_id:
            search_id = objective_function.__name__
        return search_id

    @staticmethod
    def check_list(search_space):
        for key in search_space.keys():
            search_dim = search_space[key]

            error_msg = "Value in '{}' of search space dictionary must be of type list".format(
                key
            )
            if not isinstance(search_dim, list):
                print("Warning", error_msg)
                # raise ValueError(error_msg)

    def add_search(
        self,
        objective_function: callable,
        search_space: Dict[str, list],
        n_iter: int,
        search_id=None,
        optimizer: Union[str, Type[RandomSearchOptimizer]] = "default",
        n_jobs: int = 1,
        initialize: Dict[str, int] = {"grid": 4, "random": 2, "vertices": 4},
        constraints: List[callable] = None,
        pass_through: Dict = None,
        callbacks: Dict[str, callable] = None,
        catch: Dict = None,
        max_score: float = None,
        early_stopping: Dict = None,
        random_state: int = None,
        memory: Union[str, bool] = "share",
        memory_warm_start: pd.DataFrame = None,
    ):
        """
        Add a new optimization search process with specified parameters.

        Parameters:
        - objective_function: The objective function to optimize.
        - search_space: Dictionary defining the search space for optimization.
        - n_iter: Number of iterations for the optimization process.
        - search_id: Identifier for the search process (default: None).
        - optimizer: The optimizer to use for the search process (default: "default").
        - n_jobs: Number of parallel jobs to run (default: 1).
        - initialize: Dictionary specifying initialization parameters (default: {"grid": 4, "random": 2, "vertices": 4}).
        - constraints: List of constraint functions (default: None).
        - pass_through: Dictionary of additional parameters to pass through (default: None).
        - callbacks: Dictionary of callback functions (default: None).
        - catch: Dictionary of exceptions to catch during optimization (default: None).
        - max_score: Maximum score to achieve (default: None).
        - early_stopping: Dictionary specifying early stopping criteria (default: None).
        - random_state: Seed for random number generation (default: None).
        - memory: Option to share memory between processes (default: "share").
        - memory_warm_start: DataFrame containing warm start memory (default: None).
        """

        self.check_list(search_space)

        constraints = constraints or []
        pass_through = pass_through or {}
        callbacks = callbacks or {}
        catch = catch or {}
        early_stopping = early_stopping or {}

        optimizer = self._default_opt(optimizer)
        search_id = self._default_search_id(search_id, objective_function)
        s_space = SearchSpace(search_space)

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

        n_jobs = mp.cpu_count() if n_jobs == -1 else n_jobs

        for _ in range(n_jobs):
            nth_process = len(self.opt_pros)
            self.opt_pros[nth_process] = optimizer

    def _print_info(self):
        print_res = PrintResults(self.opt_pros, self.verbosity)

        if self.verbosity:
            for _ in range(len(self.opt_pros)):
                print("")

        for results in self.results_list:
            nth_process = results["nth_process"]
            print_res.print_process(results, nth_process)

    def run(self, max_time: float = None):
        """
        Run the optimization process with an optional maximum time limit.

        Args:
            max_time (float, optional): Maximum time limit for the optimization process. Defaults to None.
        """

        self._create_shared_memory()

        for opt in self.opt_pros.values():
            opt.max_time = max_time

        self.results_list = run_search(
            self.opt_pros, self.distribution, self.n_processes
        )

        self.results_ = Results(self.results_list, self.opt_pros)

        self._print_info()

    def best_para(self, id_):
        """
        Retrieve the best parameters for a specific ID from the results.

        Parameters:
        - id_ (int): The ID of the parameters to retrieve.

        Returns:
        - Union[Dict[str, Union[int, float]], None]: The best parameters for the specified ID if found, otherwise None.

        Raises:
        - ValueError: If the objective function name is not recognized.
        """

        return self.results_.best_para(id_)

    def best_score(self, id_):
        """
        Return the best score for a specific ID from the results.

        Parameters:
        - id_ (int): The ID for which the best score is requested.
        """

        return self.results_.best_score(id_)

    def search_data(self, id_, times=False):
        """
        Retrieve search data for a specific ID from the results. Optionally exclude evaluation and iteration times if 'times' is set to False.

        Parameters:
        - id_ (int): The ID of the search data to retrieve.
        - times (bool, optional): Whether to exclude evaluation and iteration times. Defaults to False.

        Returns:
        - pd.DataFrame: The search data for the specified ID.

        columns are

            * "score" : float - The score of the search
            * "n_columns" : int - The number of columns in the search space
            * "metadata" : dict - The metadata returned by the search

        each row is a search iteration

        index is RangeIndex
        """

        search_data_ = self.results_.search_data(id_)

        if times == False:
            search_data_.drop(
                labels=["eval_times", "iter_times"],
                axis=1,
                inplace=True,
                errors="ignore",
            )
        return search_data_


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5
6			import copy
7			import multiprocessing as mp
8			import pandas as pd
9
10			from typing import Union, List, Dict, Type
11
12			from .optimizers import RandomSearchOptimizer
13			from .run_search import run_search
14
15			from .results import Results
16			from .print_results import PrintResults
17			from .search_space import SearchSpace
18
19
20			class Hyperactive:
21			"""
22			Initialize the Hyperactive class to manage optimization processes.
23
24			Parameters:
25			- verbosity: List of verbosity levels (default: ["progress_bar", "print_results", "print_times"])
26			- distribution: String indicating the distribution method (default: "multiprocessing")
27			- n_processes: Number of processes to run in parallel or "auto" to determine automatically (default: "auto")
28
29			Methods:
30			- add_search: Add a new optimization search process with specified parameters
31			- run: Execute the optimization searches
32			- best_para: Get the best parameters for a specific search
33			- best_score: Get the best score for a specific search
34			- search_data: Get the search data for a specific search
35			"""
36
37			def __init__(
38			self,
39			verbosity: list = ["progress_bar", "print_results", "print_times"],
40			distribution: str = "multiprocessing",
41			n_processes: Union[str, int] = "auto",
42			):
43			super().__init__()
44			if verbosity is False:
45			verbosity = []
46
47			self.verbosity = verbosity
48			self.distribution = distribution
49			self.n_processes = n_processes
50
51			self.opt_pros = {}
52
53			def _create_shared_memory(self):
54			_bundle_opt_processes = {}
55
56			for opt_pros in self.opt_pros.values():
57			if opt_pros.memory != "share":
58			continue
59			name = opt_pros.objective_function.__name__
60
61			_bundle_opt_processes.setdefault(name, []).append(opt_pros)
62
63			for opt_pros_l in _bundle_opt_processes.values():
64			# Check if the lengths of the search spaces of all optimizers in the list are the same.
65			if (
66			len(set(len(opt_pros.s_space()) for opt_pros in opt_pros_l))
67			== 1
68			):
69			manager = mp.Manager() # get new manager.dict
70			shared_memory = manager.dict()
71			for opt_pros in opt_pros_l:
72			opt_pros.memory = shared_memory
73			else:
74			for opt_pros in opt_pros_l:
75			opt_pros.memory = opt_pros_l[
76			0
77			].memory # get same manager.dict
78
79			@staticmethod
80			def _default_opt(optimizer):
81			if isinstance(optimizer, str):
82			if optimizer == "default":
83			optimizer = RandomSearchOptimizer()
84			return copy.deepcopy(optimizer)
85
86			@staticmethod
87			def _default_search_id(search_id, objective_function):
88			if not search_id:
89			search_id = objective_function.__name__
90			return search_id
91
92			@staticmethod
93			def check_list(search_space):
94			for key in search_space.keys():
95			search_dim = search_space[key]
96
97			error_msg = "Value in '{}' of search space dictionary must be of type list".format(
98			key
99			)
100			if not isinstance(search_dim, list):
101			print("Warning", error_msg)
102			# raise ValueError(error_msg)
103
104			def add_search(
105			self,
106			objective_function: callable,
107			search_space: Dict[str, list],
108			n_iter: int,
109			search_id=None,
110			optimizer: Union[str, Type[RandomSearchOptimizer]] = "default",
111			n_jobs: int = 1,
112			initialize: Dict[str, int] = {"grid": 4, "random": 2, "vertices": 4},
113			constraints: List[callable] = None,
114			pass_through: Dict = None,
115			callbacks: Dict[str, callable] = None,
116			catch: Dict = None,
117			max_score: float = None,
118			early_stopping: Dict = None,
119			random_state: int = None,
120			memory: Union[str, bool] = "share",
121			memory_warm_start: pd.DataFrame = None,
122			):
123			"""
124			Add a new optimization search process with specified parameters.
125
126			Parameters:
127			- objective_function: The objective function to optimize.
128			- search_space: Dictionary defining the search space for optimization.
129			- n_iter: Number of iterations for the optimization process.
130			- search_id: Identifier for the search process (default: None).
131			- optimizer: The optimizer to use for the search process (default: "default").
132			- n_jobs: Number of parallel jobs to run (default: 1).
133			- initialize: Dictionary specifying initialization parameters (default: {"grid": 4, "random": 2, "vertices": 4}).
134			- constraints: List of constraint functions (default: None).
135			- pass_through: Dictionary of additional parameters to pass through (default: None).
136			- callbacks: Dictionary of callback functions (default: None).
137			- catch: Dictionary of exceptions to catch during optimization (default: None).
138			- max_score: Maximum score to achieve (default: None).
139			- early_stopping: Dictionary specifying early stopping criteria (default: None).
140			- random_state: Seed for random number generation (default: None).
141			- memory: Option to share memory between processes (default: "share").
142			- memory_warm_start: DataFrame containing warm start memory (default: None).
143			"""
144
145			self.check_list(search_space)
146
147			constraints = constraints or []
148			pass_through = pass_through or {}
149			callbacks = callbacks or {}
150			catch = catch or {}
151			early_stopping = early_stopping or {}
152
153			optimizer = self._default_opt(optimizer)
154			search_id = self._default_search_id(search_id, objective_function)
155			s_space = SearchSpace(search_space)
156
157			optimizer.setup_search(
158			objective_function=objective_function,
159			s_space=s_space,
160			n_iter=n_iter,
161			initialize=initialize,
162			constraints=constraints,
163			pass_through=pass_through,
164			callbacks=callbacks,
165			catch=catch,
166			max_score=max_score,
167			early_stopping=early_stopping,
168			random_state=random_state,
169			memory=memory,
170			memory_warm_start=memory_warm_start,
171			verbosity=self.verbosity,
172			)
173
174			n_jobs = mp.cpu_count() if n_jobs == -1 else n_jobs
175
176			for _ in range(n_jobs):
177			nth_process = len(self.opt_pros)
178			self.opt_pros[nth_process] = optimizer
179
180			def _print_info(self):
181			print_res = PrintResults(self.opt_pros, self.verbosity)
182
183			if self.verbosity:
184			for _ in range(len(self.opt_pros)):
185			print("")
186
187			for results in self.results_list:
188			nth_process = results["nth_process"]
189			print_res.print_process(results, nth_process)
190
191			def run(self, max_time: float = None):
192			"""
193			Run the optimization process with an optional maximum time limit.
194
195			Args:
196			max_time (float, optional): Maximum time limit for the optimization process. Defaults to None.
197			"""
198
199			self._create_shared_memory()
200
201			for opt in self.opt_pros.values():
202			opt.max_time = max_time
203
204			self.results_list = run_search(
205			self.opt_pros, self.distribution, self.n_processes
206			)
207
208			self.results_ = Results(self.results_list, self.opt_pros)
209
210			self._print_info()
211
212			def best_para(self, id_):
213			"""
214			Retrieve the best parameters for a specific ID from the results.
215
216			Parameters:
217			- id_ (int): The ID of the parameters to retrieve.
218
219			Returns:
220			- Union[Dict[str, Union[int, float]], None]: The best parameters for the specified ID if found, otherwise None.
221
222			Raises:
223			- ValueError: If the objective function name is not recognized.
224			"""
225
226			return self.results_.best_para(id_)
227
228			def best_score(self, id_):
229			"""
230			Return the best score for a specific ID from the results.
231
232			Parameters:
233			- id_ (int): The ID for which the best score is requested.
234			"""
235
236			return self.results_.best_score(id_)
237
238			def search_data(self, id_, times=False):
239			"""
240			Retrieve search data for a specific ID from the results. Optionally exclude evaluation and iteration times if 'times' is set to False.
241
242			Parameters:
243			- id_ (int): The ID of the search data to retrieve.
244			- times (bool, optional): Whether to exclude evaluation and iteration times. Defaults to False.
245
246			Returns:
247			- pd.DataFrame: The search data for the specified ID.
248
249			columns are
250
251			* "score" : float - The score of the search
252			* "n_columns" : int - The number of columns in the search space
253			* "metadata" : dict - The metadata returned by the search
254
255			each row is a search iteration
256
257			index is RangeIndex
258			"""
259
260			search_data_ = self.results_.search_data(id_)
261
262			if times == False:
263			search_data_.drop(
264			labels=["eval_times", "iter_times"],
265			axis=1,
266			inplace=True,
267			errors="ignore",
268			)
269			return search_data_
270

SimonBlanke / Hyperactive

Hyperactive._create_shared_memory() B last analyzed 2025-06-23 18:24 UTC

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Hyperactive._create_shared_memory() B
last analyzed 2025-06-23 18:24 UTC