hyperactive.hyperactive.Hyperactive.search() - Code Metrics - Inspection of "bug fix ray" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 3e621d...6f6c3c )

by Simon

created 2019-11-27 16:28 UTC

hyperactive.hyperactive.Hyperactive.search() B

↳ Parent: hyperactive.hyperactive

Complexity

Conditions

Size

Total Lines	54
Code Lines	37

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	37
dl	0
loc	54
rs	8.9919
c	0
b	0
f	0
cc	4
nop	8

How to fix Long Method Many Parameters

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

import time
import warnings

from .main_args import MainArgs
from .opt_args import Arguments

from . import (
    HillClimbingOptimizer,
    StochasticHillClimbingOptimizer,
    TabuOptimizer,
    RandomSearchOptimizer,
    RandomRestartHillClimbingOptimizer,
    RandomAnnealingOptimizer,
    SimulatedAnnealingOptimizer,
    StochasticTunnelingOptimizer,
    ParallelTemperingOptimizer,
    ParticleSwarmOptimizer,
    EvolutionStrategyOptimizer,
    BayesianOptimizer,
)


def stop_warnings():
    # because sklearn warnings are annoying when they appear 100 times
    def warn(*args, **kwargs):
        pass

    import warnings

    warnings.warn = warn


class Hyperactive:
    """
    Hyperactive main class
    """

    def __init__(self, X, y, memory=True, random_state=1, verbosity=2, warnings=False):
        self._main_args_ = MainArgs(X, y, memory, random_state, verbosity)

        if not warnings:
            stop_warnings()

        self.optimizer_dict = {
            "HillClimbing": HillClimbingOptimizer,
            "StochasticHillClimbing": StochasticHillClimbingOptimizer,
            "TabuSearch": TabuOptimizer,
            "RandomSearch": RandomSearchOptimizer,
            "RandomRestartHillClimbing": RandomRestartHillClimbingOptimizer,
            "RandomAnnealing": RandomAnnealingOptimizer,
            "SimulatedAnnealing": SimulatedAnnealingOptimizer,
            "StochasticTunneling": StochasticTunnelingOptimizer,
            "ParallelTempering": ParallelTemperingOptimizer,
            "ParticleSwarm": ParticleSwarmOptimizer,
            "EvolutionStrategy": EvolutionStrategyOptimizer,
            "Bayesian": BayesianOptimizer,
        }

    def search(
        self,
        search_config,
        max_time=None,
        n_iter=10,
        optimizer="RandomSearch",
        n_jobs=1,
        warm_start=False,
        scatter_init=False,
    ):
        """
        run search
        """

        start_time = time.time()

        self._main_args_.search_args(
            search_config, max_time, n_iter, optimizer, n_jobs, warm_start, scatter_init
        )
        self._opt_args_ = Arguments(self._main_args_.opt_para)
        optimizer_class = self.optimizer_dict[self._main_args_.optimizer]

        try:
            import ray

            if ray.is_initialized():
                ray_ = True
            else:
                ray_ = False
        except ImportError:
            warnings.warn("failed to import ray", ImportWarning)
            ray_ = False

        if ray_:
            optimizer_class = ray.remote(optimizer_class)
            opts = [
                optimizer_class.remote(self._main_args_, self._opt_args_)
                for job in range(self._main_args_.n_jobs)
            ]
            searches = [
                opt.search.remote(job, ray_=ray_) for job, opt in enumerate(opts)
            ]
            ray.get(searches)
        else:
            self._optimizer_ = optimizer_class(self._main_args_, self._opt_args_)
            self._optimizer_.search()

        self.results_params = self._optimizer_.results_params
        self.results_models = self._optimizer_.results_models

        self.pos_list = self._optimizer_.pos_list
        self.score_list = self._optimizer_.score_list

        self.total_time = time.time() - start_time

    def get_total_time(self):
        return self.total_time

    def get_eval_time(self):
        return self._optimizer_.eval_time

    def save_report(self):
        pass


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import time
6			import warnings
7
8			from .main_args import MainArgs
9			from .opt_args import Arguments
10
11			from . import (
12			HillClimbingOptimizer,
13			StochasticHillClimbingOptimizer,
14			TabuOptimizer,
15			RandomSearchOptimizer,
16			RandomRestartHillClimbingOptimizer,
17			RandomAnnealingOptimizer,
18			SimulatedAnnealingOptimizer,
19			StochasticTunnelingOptimizer,
20			ParallelTemperingOptimizer,
21			ParticleSwarmOptimizer,
22			EvolutionStrategyOptimizer,
23			BayesianOptimizer,
24			)
25
26
27			def stop_warnings():
28			# because sklearn warnings are annoying when they appear 100 times
29			def warn(args, *kwargs):
30			pass
31
32			import warnings
33
34			warnings.warn = warn
35
36
37			class Hyperactive:
38			"""
39			Hyperactive main class
40			"""
41
42			def __init__(self, X, y, memory=True, random_state=1, verbosity=2, warnings=False):
43			self._main_args_ = MainArgs(X, y, memory, random_state, verbosity)
44
45			if not warnings:
46			stop_warnings()
47
48			self.optimizer_dict = {
49			"HillClimbing": HillClimbingOptimizer,
50			"StochasticHillClimbing": StochasticHillClimbingOptimizer,
51			"TabuSearch": TabuOptimizer,
52			"RandomSearch": RandomSearchOptimizer,
53			"RandomRestartHillClimbing": RandomRestartHillClimbingOptimizer,
54			"RandomAnnealing": RandomAnnealingOptimizer,
55			"SimulatedAnnealing": SimulatedAnnealingOptimizer,
56			"StochasticTunneling": StochasticTunnelingOptimizer,
57			"ParallelTempering": ParallelTemperingOptimizer,
58			"ParticleSwarm": ParticleSwarmOptimizer,
59			"EvolutionStrategy": EvolutionStrategyOptimizer,
60			"Bayesian": BayesianOptimizer,
61			}
62
63			def search(
64			self,
65			search_config,
66			max_time=None,
67			n_iter=10,
68			optimizer="RandomSearch",
69			n_jobs=1,
70			warm_start=False,
71			scatter_init=False,
72			):
73			"""
74			run search
75			"""
76
77			start_time = time.time()
78
79			self._main_args_.search_args(
80			search_config, max_time, n_iter, optimizer, n_jobs, warm_start, scatter_init
81			)
82			self._opt_args_ = Arguments(self._main_args_.opt_para)
83			optimizer_class = self.optimizer_dict[self._main_args_.optimizer]
84
85			try:
86			import ray
87
88			if ray.is_initialized():
89			ray_ = True
90			else:
91			ray_ = False
92			except ImportError:
93			warnings.warn("failed to import ray", ImportWarning)
94			ray_ = False
95
96			if ray_:
97			optimizer_class = ray.remote(optimizer_class)
98			opts = [
99			optimizer_class.remote(self._main_args_, self._opt_args_)
100			for job in range(self._main_args_.n_jobs)
101			]
102			searches = [
103			opt.search.remote(job, ray_=ray_) for job, opt in enumerate(opts)
104			]
105			ray.get(searches)
106			else:
107			self._optimizer_ = optimizer_class(self._main_args_, self._opt_args_)
108			self._optimizer_.search()
109
110			self.results_params = self._optimizer_.results_params
111			self.results_models = self._optimizer_.results_models
112
113			self.pos_list = self._optimizer_.pos_list
114			self.score_list = self._optimizer_.score_list
115
116			self.total_time = time.time() - start_time
117
118			def get_total_time(self):
119			return self.total_time
120
121			def get_eval_time(self):
122			return self._optimizer_.eval_time
123
124			def save_report(self):
125			pass
126

SimonBlanke / Hyperactive

Push — master ( 3e621d...6f6c3c )

hyperactive.hyperactive.Hyperactive.search() B

Complexity

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How to fix Long Method Many Parameters

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