hyperactive.distribution.dist_ray() - Code Metrics - Inspection of "refactor distribution code" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Passed

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by Simon

created 2020-01-16 07:21 UTC

hyperactive.distribution.dist_ray() A

↳ Parent: hyperactive.distribution

Complexity

Conditions

Size

Total Lines	14
Code Lines	9

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	9
nop	3
dl	0
loc	14
rs	9.95
c	0
b	0
f	0

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

import warnings

def try_ray_import():
    try:
        import ray

        if ray.is_initialized():
            rayInit = True
        else:
            rayInit = False
    except ImportError:
        warnings.warn("failed to import ray", ImportWarning)
        ray = None
        rayInit = False

    return ray, rayInit


def dist(optimizer_class, _main_args_, _opt_args_):
    ray, rayInit = try_ray_import()

    if rayInit:
        dist_ray(optimizer_class, _main_args_, _opt_args_)
    else:
        dist_default(optimizer_class, _main_args_, _opt_args_)


def dist_default(optimizer_class, _main_args_, _opt_args_):
    _optimizer_ = optimizer_class(_main_args_, _opt_args_)
    params_results, pos_list, score_list = (
        _optimizer_.search()
    )

    # print("params_results", params_results)

def dist_ray(optimizer_class, _main_args_, _opt_args_):
    optimizer_class = ray.remote(optimizer_class)

    opts = [
        optimizer_class.remote(_main_args_, _opt_args_)
        for job in range(_main_args_.n_jobs)
    ]
    searches = [
        opt.search.remote(job, rayInit=rayInit) for job, opt in enumerate(opts)

    ]
    params_results, pos_list, score_list = ray.get(searches)[0]

    # print("params_results", params_results)

    ray.shutdown()

1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import warnings
6
7			def try_ray_import():
8			try:
9			import ray
10
11			if ray.is_initialized():
12			rayInit = True
13			else:
14			rayInit = False
15			except ImportError:
16			warnings.warn("failed to import ray", ImportWarning)
17			ray = None
18			rayInit = False
19
20			return ray, rayInit
21
22
23			def dist(optimizer_class, _main_args_, _opt_args_):
24			ray, rayInit = try_ray_import()
25
26			if rayInit:
27			dist_ray(optimizer_class, _main_args_, _opt_args_)
28			else:
29			dist_default(optimizer_class, _main_args_, _opt_args_)
30
31
32			def dist_default(optimizer_class, _main_args_, _opt_args_):
33			_optimizer_ = optimizer_class(_main_args_, _opt_args_)
34			params_results, pos_list, score_list = (
35			_optimizer_.search()
36			)
37
38			# print("params_results", params_results)
39
40			def dist_ray(optimizer_class, _main_args_, _opt_args_):
41			optimizer_class = ray.remote(optimizer_class)
			0 ignored issues – show Comprehensibility Best Practice introduced 2020-01-16 07:22 UTC by Report Bug Copy Issue Report The variable `ray` does not seem to be defined. Loading history...
42			opts = [
43			optimizer_class.remote(_main_args_, _opt_args_)
44			for job in range(_main_args_.n_jobs)
45			]
46			searches = [
47			opt.search.remote(job, rayInit=rayInit) for job, opt in enumerate(opts)
			0 ignored issues – show Comprehensibility Best Practice introduced 2020-01-16 07:22 UTC by Report Bug Copy Issue Report The variable `rayInit` does not seem to be defined. Loading history...
48			]
49			params_results, pos_list, score_list = ray.get(searches)[0]
50
51			# print("params_results", params_results)
52
53			ray.shutdown()

SimonBlanke / Hyperactive

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hyperactive.distribution.dist_ray() A

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