gradient_free_optimizers.search.Search.print_info() - Code Metrics - Inspection of "reformat requirements" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( 7aa4c7...2b6f90 )

by Simon

created 2021-01-11 15:06 UTC

Search.print_info() A

↳ Parent: gradient_free_optimizers.search

Complexity

Conditions

Size

Total Lines	2
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	2
nop	2
dl	0
loc	2
rs	10
c	0
b	0
f	0

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

import time
import random

import numpy as np
import pandas as pd

from .init_positions import Initializer
from .progress_bar import ProgressBarLVL0, ProgressBarLVL1
from .times_tracker import TimesTracker
from .memory import Memory
from .print_info import print_info


def time_exceeded(start_time, max_time):
    run_time = time.time() - start_time
    return max_time and run_time > max_time


def score_exceeded(score_best, max_score):
    return max_score and score_best >= max_score


def set_random_seed(nth_process, random_state):
    """
    Sets the random seed separately for each thread
    (to avoid getting the same results in each thread)
    """
    if nth_process is None:
        nth_process = 0

    if random_state is None:
        random_state = np.random.randint(0, high=2 ** 32 - 2)

    random.seed(random_state + nth_process)
    np.random.seed(random_state + nth_process)


class Search(TimesTracker):
    def __init__(self):
        super().__init__()

        self.optimizers = []
        self.new_results_list = []
        self.all_results_list = []

    @TimesTracker.eval_time
    def _score(self, pos):
        return self.score(pos)

    @TimesTracker.iter_time
    def _initialization(self, init_pos, nth_iter):
        self.init_pos(init_pos)

        score_new = self._score(init_pos)
        self.evaluate(score_new)

        self.p_bar.update(score_new, init_pos, nth_iter)

    @TimesTracker.iter_time
    def _iteration(self, nth_iter):
        pos_new = self.iterate()

        score_new = self._score(pos_new)
        self.evaluate(score_new)

        self.p_bar.update(score_new, pos_new, nth_iter)

    def _init_search(self):
        if "progress_bar" in self.verbosity:
            self.p_bar = ProgressBarLVL1(
                self.nth_process, self.n_iter, self.objective_function
            )
        else:
            self.p_bar = ProgressBarLVL0(
                self.nth_process, self.n_iter, self.objective_function
            )

        set_random_seed(self.nth_process, self.random_state)

        # get init positions
        init = Initializer(self.conv)
        init_positions = init.set_pos(self.initialize)

        return init_positions

    def _early_stop(self):
        if time_exceeded(self.start_time, self.max_time):
            return True
        elif score_exceeded(self.p_bar.score_best, self.max_score):
            return True
        else:
            return False

    def _init_verb_dict(self, verb_dict):
        if verb_dict in [None, False]:
            return {
                "progress_bar": False,
                "print_results": False,
                "print_times": False,
            }

        verb_default = {
            "progress_bar": True,
            "print_results": True,
            "print_times": True,
        }

        for verb_key in verb_default.keys():
            if verb_key not in verb_dict:
                verb_dict[verb_key] = verb_default[verb_key]

        return verb_dict

    def print_info(self, *args):
        print_info(*args)

    def search(
        self,
        objective_function,
        n_iter,
        max_time=None,
        max_score=None,
        memory=True,
        memory_warm_start=None,
        verbosity=["progress_bar", "print_results", "print_times"],
        random_state=None,
        nth_process=None,
    ):
        self.start_time = time.time()

        if verbosity is False:
            verbosity = []

        self.objective_function = objective_function
        self.n_iter = n_iter
        self.max_time = max_time
        self.max_score = max_score
        self.memory = memory
        self.memory_warm_start = memory_warm_start
        self.verbosity = verbosity
        self.random_state = random_state
        self.nth_process = nth_process

        init_positions = self._init_search()

        if memory is True:
            mem = Memory(memory_warm_start, self.conv)
            self.score = self.results_mang.score(
                mem.memory(objective_function)
            )
        else:
            self.score = self.results_mang.score(objective_function)

        # loop to initialize N positions
        for init_pos, nth_iter in zip(init_positions, range(n_iter)):
            if self._early_stop():
                break
            self._initialization(init_pos, nth_iter)

        # loop to do the iterations
        for nth_iter in range(len(init_positions), n_iter):
            if self._early_stop():
                break
            self._iteration(nth_iter)

        self.results = pd.DataFrame(self.results_mang.results_list)

        self.best_score = self.p_bar.score_best
        self.best_value = self.conv.position2value(self.p_bar.pos_best)
        self.best_para = self.conv.value2para(self.best_value)

        self.results["eval_time"] = self.eval_times
        self.results["iter_time"] = self.iter_times

        if memory is not False:
            self.memory_dict = mem.memory_dict

        else:
            self.memory_dict = {}

        self.p_bar.close()

        self.print_info(
            verbosity,
            self.objective_function,
            self.best_score,
            self.best_para,
            self.eval_times,
            self.iter_times,
            self.n_iter,
        )



1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import time
6			import random
7
8			import numpy as np
9			import pandas as pd
10
11			from .init_positions import Initializer
12			from .progress_bar import ProgressBarLVL0, ProgressBarLVL1
13			from .times_tracker import TimesTracker
14			from .memory import Memory
15			from .print_info import print_info
16
17
18			def time_exceeded(start_time, max_time):
19			run_time = time.time() - start_time
20			return max_time and run_time > max_time
21
22
23			def score_exceeded(score_best, max_score):
24			return max_score and score_best >= max_score
25
26
27			def set_random_seed(nth_process, random_state):
28			"""
29			Sets the random seed separately for each thread
30			(to avoid getting the same results in each thread)
31			"""
32			if nth_process is None:
33			nth_process = 0
34
35			if random_state is None:
36			random_state = np.random.randint(0, high=2 ** 32 - 2)
37
38			random.seed(random_state + nth_process)
39			np.random.seed(random_state + nth_process)
40
41
42			class Search(TimesTracker):
43			def __init__(self):
44			super().__init__()
45
46			self.optimizers = []
47			self.new_results_list = []
48			self.all_results_list = []
49
50			@TimesTracker.eval_time
51			def _score(self, pos):
52			return self.score(pos)
53
54			@TimesTracker.iter_time
55			def _initialization(self, init_pos, nth_iter):
56			self.init_pos(init_pos)
57
58			score_new = self._score(init_pos)
59			self.evaluate(score_new)
60
61			self.p_bar.update(score_new, init_pos, nth_iter)
62
63			@TimesTracker.iter_time
64			def _iteration(self, nth_iter):
65			pos_new = self.iterate()
66
67			score_new = self._score(pos_new)
68			self.evaluate(score_new)
69
70			self.p_bar.update(score_new, pos_new, nth_iter)
71
72			def _init_search(self):
73			if "progress_bar" in self.verbosity:
74			self.p_bar = ProgressBarLVL1(
75			self.nth_process, self.n_iter, self.objective_function
76			)
77			else:
78			self.p_bar = ProgressBarLVL0(
79			self.nth_process, self.n_iter, self.objective_function
80			)
81
82			set_random_seed(self.nth_process, self.random_state)
83
84			# get init positions
85			init = Initializer(self.conv)
86			init_positions = init.set_pos(self.initialize)
87
88			return init_positions
89
90			def _early_stop(self):
91			if time_exceeded(self.start_time, self.max_time):
92			return True
93			elif score_exceeded(self.p_bar.score_best, self.max_score):
94			return True
95			else:
96			return False
97
98			def _init_verb_dict(self, verb_dict):
99			if verb_dict in [None, False]:
100			return {
101			"progress_bar": False,
102			"print_results": False,
103			"print_times": False,
104			}
105
106			verb_default = {
107			"progress_bar": True,
108			"print_results": True,
109			"print_times": True,
110			}
111
112			for verb_key in verb_default.keys():
113			if verb_key not in verb_dict:
114			verb_dict[verb_key] = verb_default[verb_key]
115
116			return verb_dict
117
118			def print_info(self, *args):
119			print_info(*args)
120
121			def search(
122			self,
123			objective_function,
124			n_iter,
125			max_time=None,
126			max_score=None,
127			memory=True,
128			memory_warm_start=None,
129			verbosity=["progress_bar", "print_results", "print_times"],
130			random_state=None,
131			nth_process=None,
132			):
133			self.start_time = time.time()
134
135			if verbosity is False:
136			verbosity = []
137
138			self.objective_function = objective_function
139			self.n_iter = n_iter
140			self.max_time = max_time
141			self.max_score = max_score
142			self.memory = memory
143			self.memory_warm_start = memory_warm_start
144			self.verbosity = verbosity
145			self.random_state = random_state
146			self.nth_process = nth_process
147
148			init_positions = self._init_search()
149
150			if memory is True:
151			mem = Memory(memory_warm_start, self.conv)
152			self.score = self.results_mang.score(
153			mem.memory(objective_function)
154			)
155			else:
156			self.score = self.results_mang.score(objective_function)
157
158			# loop to initialize N positions
159			for init_pos, nth_iter in zip(init_positions, range(n_iter)):
160			if self._early_stop():
161			break
162			self._initialization(init_pos, nth_iter)
163
164			# loop to do the iterations
165			for nth_iter in range(len(init_positions), n_iter):
166			if self._early_stop():
167			break
168			self._iteration(nth_iter)
169
170			self.results = pd.DataFrame(self.results_mang.results_list)
171
172			self.best_score = self.p_bar.score_best
173			self.best_value = self.conv.position2value(self.p_bar.pos_best)
174			self.best_para = self.conv.value2para(self.best_value)
175
176			self.results["eval_time"] = self.eval_times
177			self.results["iter_time"] = self.iter_times
178
179			if memory is not False:
180			self.memory_dict = mem.memory_dict
			0 ignored issues – show introduced 2021-01-11 15:10 UTC by Report Bug Copy Issue Report The variable `mem` does not seem to be defined for all execution paths. Loading history...
181			else:
182			self.memory_dict = {}
183
184			self.p_bar.close()
185
186			self.print_info(
187			verbosity,
188			self.objective_function,
189			self.best_score,
190			self.best_para,
191			self.eval_times,
192			self.iter_times,
193			self.n_iter,
194			)
195
196

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 7aa4c7...2b6f90 )

Search.print_info() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like