gradient_free_optimizers.progress_bar.ProgressBarLVL1._tqdm_dict() - Code Metrics - Inspection of "simpler move_random function + new attributes" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-08-19 18:43 UTC

ProgressBarLVL1._tqdm_dict() A

↳ Parent: gradient_free_optimizers.progress_bar

Complexity

Conditions

Size

Total Lines	7
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	6
nop	4
dl	0
loc	7
rs	10
c	0
b	0
f	0

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

import numpy as np
from tqdm.auto import tqdm


class ProgressBarLVL0:
    def __init__(self):
        pass

    def init(self, nth_process, n_iter, obj_func):
        pass

    def update(self, iter, score_new):
        pass

    def close(self):
        pass

    def _tqdm_dict(self, nth_process, n_iter, obj_func):
        pass


class ProgressBarLVL1:
    def __init__(self):
        self.best_since_iter = 0
        self.score_best = -np.inf
        # tqdm.set_lock(tqdm.get_lock())

    def init(self, nth_process, n_iter, obj_func):
        self._tqdm = tqdm(**self._tqdm_dict(nth_process, n_iter, obj_func))

    def update(self, iter, score_new):
        self._tqdm.update(iter)

        if score_new > self.score_best:
            self.score_best = score_new
            self.best_since_iter = self._tqdm.n
            self._tqdm.set_postfix(
                best_score=str(score_new), best_since_iter=self.best_since_iter
            )

    def close(self):
        self._tqdm.close()

    def _tqdm_dict(self, nth_process, n_iter, obj_func):
        """Generates the parameter dict for tqdm in the iteration-loop of each optimizer"""
        return {
            "total": n_iter,
            "desc": "Process " + str(nth_process) + " -> " + obj_func.__name__,
            "position": nth_process,
            "leave": True,
        }



1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import numpy as np
6			from tqdm.auto import tqdm
7
8
9			class ProgressBarLVL0:
10			def __init__(self):
11			pass
12
13			def init(self, nth_process, n_iter, obj_func):
14			pass
15
16			def update(self, iter, score_new):
17			pass
18
19			def close(self):
20			pass
21
22			def _tqdm_dict(self, nth_process, n_iter, obj_func):
23			pass
24
25
26			class ProgressBarLVL1:
27			def __init__(self):
28			self.best_since_iter = 0
29			self.score_best = -np.inf
30			# tqdm.set_lock(tqdm.get_lock())
31
32			def init(self, nth_process, n_iter, obj_func):
33			self._tqdm = tqdm(**self._tqdm_dict(nth_process, n_iter, obj_func))
34
35			def update(self, iter, score_new):
36			self._tqdm.update(iter)
37
38			if score_new > self.score_best:
39			self.score_best = score_new
40			self.best_since_iter = self._tqdm.n
41			self._tqdm.set_postfix(
42			best_score=str(score_new), best_since_iter=self.best_since_iter
43			)
44
45			def close(self):
46			self._tqdm.close()
47
48			def _tqdm_dict(self, nth_process, n_iter, obj_func):
49			"""Generates the parameter dict for tqdm in the iteration-loop of each optimizer"""
50			return {
51			"total": n_iter,
52			"desc": "Process " + str(nth_process) + " -> " + obj_func.__name__,
53			"position": nth_process,
54			"leave": True,
55			}
56
57

SimonBlanke / Gradient-Free-Optimizers

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ProgressBarLVL1._tqdm_dict() A

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