hyperactive.hyperactive.Hyperactive.__init__() - Code Metrics - Inspection of "rm opt-dev from dev-branch" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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hyperactive.hyperactive.Hyperactive.init() A

↳ Parent: hyperactive.hyperactive

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Total Lines	60
Code Lines	20

Duplication

Lines	0
Ratio	0 %

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Metric	Value
eloc	20
dl	0
loc	60
rs	9.4
c	0
b	0
f	0
cc	1
nop	3

How to fix Long Method

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

import time

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


class Hyperactive:
    def __init__(self, *args, **kwargs):

        """

        Parameters
        ----------

        search_config: dict
            A dictionary providing the model and hyperparameter search space for the
            optimization process.
        n_iter: int
            The number of iterations the optimizer performs.
        metric: string, optional (default: "accuracy")
            The metric the model is evaluated by.
        n_jobs: int, optional (default: 1)
            The number of searches to run in parallel.
        cv: int, optional (default: 3)
            The number of folds for the cross validation.
        verbosity: int, optional (default: 1)
            Verbosity level. 1 prints out warm_start points and their scores.
        random_state: int, optional (default: None)
            Sets the random seed.
        warm_start: dict, optional (default: False)
            Dictionary that definies a start point for the optimizer.
        memory: bool, optional (default: True)
            A memory, that saves the evaluation during the optimization to save time when
            optimizer returns to position.
        scatter_init: int, optional (default: False)
            Defines the number n of random positions that should be evaluated with 1/n the
            training data, to find a better initial position.

        Returns
        -------
        None

        """

        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,
        }

        _core_ = Core(*args, **kwargs)
        _arg_ = Arguments(**_core_.opt_para)

        optimizer_class = optimizer_dict[_core_.optimizer]
        self._optimizer_ = optimizer_class(_core_, _arg_)

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

    def search(self, X, y):
        """Public method for starting the search with the training data (X, y)

        Parameters
        ----------
        X : array-like or sparse matrix of shape = [n_samples, n_features]

        y : array-like, shape = [n_samples] or [n_samples, n_outputs]

        Returns
        -------
        None
        """
        start_time = time.time()
        self._optimizer_._fit(X, y)
        self.score_best = self._optimizer_.score_best

        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 get_results(self):
        return self._optimizer_.results

    def save_report(self):
        pass


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import time
6
7			from .core import Core
8			from .opt_args import Arguments
9			from . import (
10			HillClimbingOptimizer,
11			StochasticHillClimbingOptimizer,
12			TabuOptimizer,
13			RandomSearchOptimizer,
14			RandomRestartHillClimbingOptimizer,
15			RandomAnnealingOptimizer,
16			SimulatedAnnealingOptimizer,
17			StochasticTunnelingOptimizer,
18			ParallelTemperingOptimizer,
19			ParticleSwarmOptimizer,
20			EvolutionStrategyOptimizer,
21			BayesianOptimizer,
22			)
23
24
25			class Hyperactive:
26			def __init__(self, args, *kwargs):
27
28			"""
29
30			Parameters
31			----------
32
33			search_config: dict
34			A dictionary providing the model and hyperparameter search space for the
35			optimization process.
36			n_iter: int
37			The number of iterations the optimizer performs.
38			metric: string, optional (default: "accuracy")
39			The metric the model is evaluated by.
40			n_jobs: int, optional (default: 1)
41			The number of searches to run in parallel.
42			cv: int, optional (default: 3)
43			The number of folds for the cross validation.
44			verbosity: int, optional (default: 1)
45			Verbosity level. 1 prints out warm_start points and their scores.
46			random_state: int, optional (default: None)
47			Sets the random seed.
48			warm_start: dict, optional (default: False)
49			Dictionary that definies a start point for the optimizer.
50			memory: bool, optional (default: True)
51			A memory, that saves the evaluation during the optimization to save time when
52			optimizer returns to position.
53			scatter_init: int, optional (default: False)
54			Defines the number n of random positions that should be evaluated with 1/n the
55			training data, to find a better initial position.
56
57			Returns
58			-------
59			None
60
61			"""
62
63			optimizer_dict = {
64			"HillClimbing": HillClimbingOptimizer,
65			"StochasticHillClimbing": StochasticHillClimbingOptimizer,
66			"TabuSearch": TabuOptimizer,
67			"RandomSearch": RandomSearchOptimizer,
68			"RandomRestartHillClimbing": RandomRestartHillClimbingOptimizer,
69			"RandomAnnealing": RandomAnnealingOptimizer,
70			"SimulatedAnnealing": SimulatedAnnealingOptimizer,
71			"StochasticTunneling": StochasticTunnelingOptimizer,
72			"ParallelTempering": ParallelTemperingOptimizer,
73			"ParticleSwarm": ParticleSwarmOptimizer,
74			"EvolutionStrategy": EvolutionStrategyOptimizer,
75			"Bayesian": BayesianOptimizer,
76			}
77
78			_core_ = Core(args, *kwargs)
79			_arg_ = Arguments(**_core_.opt_para)
80
81			optimizer_class = optimizer_dict[_core_.optimizer]
82			self._optimizer_ = optimizer_class(_core_, _arg_)
83
84			self.pos_list = self._optimizer_.pos_list
85			self.score_list = self._optimizer_.score_list
86
87			def search(self, X, y):
88			"""Public method for starting the search with the training data (X, y)
89
90			Parameters
91			----------
92			X : array-like or sparse matrix of shape = [n_samples, n_features]
93
94			y : array-like, shape = [n_samples] or [n_samples, n_outputs]
95
96			Returns
97			-------
98			None
99			"""
100			start_time = time.time()
101			self._optimizer_._fit(X, y)
102			self.score_best = self._optimizer_.score_best
103
104			self.total_time = time.time() - start_time
105
106			def get_total_time(self):
107			return self.total_time
108
109			def get_eval_time(self):
110			return self._optimizer_.eval_time
111
112			def get_results(self):
113			return self._optimizer_.results
114
115			def save_report(self):
116			pass
117

SimonBlanke / Hyperactive

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hyperactive.hyperactive.Hyperactive.__init__() A

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