gradient_free_optimizers.progress_bar.ProgressBarLVL1._tqdm_dict() - Code Metrics - Inspection of "previous search-info can be passed in memory dict" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-08-21 10:58 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 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, nth_process, n_iter, obj_func):
        self.best_since_iter = 0
        self.score_best = -np.inf
        # tqdm.set_lock(tqdm.get_lock())

        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 update(self, iter, score_new):
14			pass
15
16			def close(self):
17			pass
18
19			def _tqdm_dict(self, nth_process, n_iter, obj_func):
20			pass
21
22
23			class ProgressBarLVL1:
24			def __init__(self, nth_process, n_iter, obj_func):
25			self.best_since_iter = 0
26			self.score_best = -np.inf
27			# tqdm.set_lock(tqdm.get_lock())
28
29			self._tqdm = tqdm(**self._tqdm_dict(nth_process, n_iter, obj_func))
30
31			def update(self, iter, score_new):
32			self._tqdm.update(iter)
33
34			if score_new > self.score_best:
35			self.score_best = score_new
36			self.best_since_iter = self._tqdm.n
37			self._tqdm.set_postfix(
38			best_score=str(score_new), best_since_iter=self.best_since_iter
39			)
40
41			def close(self):
42			self._tqdm.close()
43
44			def _tqdm_dict(self, nth_process, n_iter, obj_func):
45			"""Generates the parameter dict for tqdm in the iteration-loop of each optimizer"""
46			return {
47			"total": n_iter,
48			"desc": "Process " + str(nth_process) + " -> " + obj_func.__name__,
49			"position": nth_process,
50			"leave": True,
51			}
52
53

SimonBlanke / Gradient-Free-Optimizers

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ProgressBarLVL1._tqdm_dict() A

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