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

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created 2019-10-22 15:08 UTC

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


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
        """
        self._optimizer_._fit(X, y)
        self.score_best = self._optimizer_.score_best

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

SimonBlanke / Hyperactive

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hyperactive.hyperactive.Hyperactive.__init__() A

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hyperactive.hyperactive.Hyperactive.init() A