gradient_free_optimizers.search.Search._iteration() - Code Metrics - Inspection of "add early_stopping to doc" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 97c085...baa350 )

by Simon

created 2021-08-29 05:59 UTC

Search._iteration() A

↳ Parent: gradient_free_optimizers.search

Complexity

Conditions

Size

Total Lines	11
Code Lines	8

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	8
nop	2
dl	0
loc	11
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 multiprocessing.managers import DictProxy

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


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 no_change(score_new_list, early_stopping):
    if "n_iter_no_change" not in early_stopping:
        print(
            "Warning n_iter_no_change-parameter must be set in order for early stopping to work"
        )
        return False

    n_iter_no_change = early_stopping["n_iter_no_change"]
    if len(score_new_list) <= n_iter_no_change:
        return False

    scores_np = np.array(score_new_list)

    max_score = max(score_new_list)
    max_index = np.argmax(scores_np)
    length_pos = len(score_new_list)

    diff = length_pos - max_index

    if diff > n_iter_no_change:
        return True

    first_n = length_pos - n_iter_no_change
    scores_first_n = score_new_list[:first_n]

    max_first_n = max(scores_first_n)

    if "tol_abs" in early_stopping and early_stopping["tol_abs"] is not None:
        tol_abs = early_stopping["tol_abs"]

        if abs(max_first_n - max_score) < tol_abs:
            return True

    if "tol_rel" in early_stopping and early_stopping["tol_rel"] is not None:
        tol_rel = early_stopping["tol_rel"]

        percent_imp = ((max_score - max_first_n) / abs(max_first_n)) * 100
        if percent_imp < tol_rel:
            return True


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 ** 31 - 2, dtype=np.int64)

    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.nth_iter = nth_iter
        self.best_score = self.p_bar.score_best

        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):
        self.nth_iter = nth_iter
        self.best_score = self.p_bar.score_best

        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):
        self.stop = False

        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)

    def _early_stop(self):
        if self.stop:
            return

        if self.max_time and time_exceeded(self.start_time, self.max_time):
            self.stop = True
        elif self.max_score and score_exceeded(self.p_bar.score_best, self.max_score):
            self.stop = True
        elif self.early_stopping and no_change(
            self.score_new_list, self.early_stopping
        ):
            self.stop = True

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

    def search(
        self,
        objective_function,
        n_iter,
        max_time=None,
        max_score=None,
        early_stopping=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.early_stopping = early_stopping
        self.memory = memory
        self.memory_warm_start = memory_warm_start
        self.verbosity = verbosity
        self.random_state = random_state
        self.nth_process = nth_process

        self._init_search()

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

        if isinstance(memory, DictProxy):
            mem = Memory(memory_warm_start, self.conv, dict_proxy=memory)
            self.score = self.results_mang.score(mem.memory(objective_function))
        elif 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(self.init_positions, range(n_iter)):
            self._early_stop()
            if self.stop:
                break
            self._initialization(init_pos, nth_iter)

        self.finish_initialization()

        # loop to do the iterations
        for nth_iter in range(len(self.init_positions), n_iter):
            self._early_stop()
            if self.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 multiprocessing.managers import DictProxy
12
13			from .progress_bar import ProgressBarLVL0, ProgressBarLVL1
14			from .times_tracker import TimesTracker
15			from .memory import Memory
16			from .print_info import print_info
17			from .init_positions import Initializer
18
19
20			def time_exceeded(start_time, max_time):
21			run_time = time.time() - start_time
22			return max_time and run_time > max_time
23
24
25			def score_exceeded(score_best, max_score):
26			return max_score and score_best >= max_score
27
28
29			def no_change(score_new_list, early_stopping):
30			if "n_iter_no_change" not in early_stopping:
31			print(
32			"Warning n_iter_no_change-parameter must be set in order for early stopping to work"
33			)
34			return False
35
36			n_iter_no_change = early_stopping["n_iter_no_change"]
37			if len(score_new_list) <= n_iter_no_change:
38			return False
39
40			scores_np = np.array(score_new_list)
41
42			max_score = max(score_new_list)
43			max_index = np.argmax(scores_np)
44			length_pos = len(score_new_list)
45
46			diff = length_pos - max_index
47
48			if diff > n_iter_no_change:
49			return True
50
51			first_n = length_pos - n_iter_no_change
52			scores_first_n = score_new_list[:first_n]
53
54			max_first_n = max(scores_first_n)
55
56			if "tol_abs" in early_stopping and early_stopping["tol_abs"] is not None:
57			tol_abs = early_stopping["tol_abs"]
58
59			if abs(max_first_n - max_score) < tol_abs:
60			return True
61
62			if "tol_rel" in early_stopping and early_stopping["tol_rel"] is not None:
63			tol_rel = early_stopping["tol_rel"]
64
65			percent_imp = ((max_score - max_first_n) / abs(max_first_n)) * 100
66			if percent_imp < tol_rel:
67			return True
68
69
70			def set_random_seed(nth_process, random_state):
71			"""
72			Sets the random seed separately for each thread
73			(to avoid getting the same results in each thread)
74			"""
75			if nth_process is None:
76			nth_process = 0
77
78			if random_state is None:
79			random_state = np.random.randint(0, high=2 ** 31 - 2, dtype=np.int64)
80
81			random.seed(random_state + nth_process)
82			np.random.seed(random_state + nth_process)
83
84
85			class Search(TimesTracker):
86			def __init__(self):
87			super().__init__()
88
89			self.optimizers = []
90			self.new_results_list = []
91			self.all_results_list = []
92
93			@TimesTracker.eval_time
94			def _score(self, pos):
95			return self.score(pos)
96
97			@TimesTracker.iter_time
98			def _initialization(self, init_pos, nth_iter):
99			self.nth_iter = nth_iter
100			self.best_score = self.p_bar.score_best
101
102			self.init_pos(init_pos)
103
104			score_new = self._score(init_pos)
105			self.evaluate(score_new)
106
107			self.p_bar.update(score_new, init_pos, nth_iter)
108
109			@TimesTracker.iter_time
110			def _iteration(self, nth_iter):
111			self.nth_iter = nth_iter
112			self.best_score = self.p_bar.score_best
113
114			pos_new = self.iterate()
115
116			score_new = self._score(pos_new)
117			self.evaluate(score_new)
118
119			self.p_bar.update(score_new, pos_new, nth_iter)
120
121			def _init_search(self):
122			self.stop = False
123
124			if "progress_bar" in self.verbosity:
125			self.p_bar = ProgressBarLVL1(
126			self.nth_process, self.n_iter, self.objective_function
127			)
128			else:
129			self.p_bar = ProgressBarLVL0(
130			self.nth_process, self.n_iter, self.objective_function
131			)
132
133			set_random_seed(self.nth_process, self.random_state)
134
135			def _early_stop(self):
136			if self.stop:
137			return
138
139			if self.max_time and time_exceeded(self.start_time, self.max_time):
140			self.stop = True
141			elif self.max_score and score_exceeded(self.p_bar.score_best, self.max_score):
142			self.stop = True
143			elif self.early_stopping and no_change(
144			self.score_new_list, self.early_stopping
145			):
146			self.stop = True
147
148			def print_info(self, *args):
149			print_info(*args)
150
151			def search(
152			self,
153			objective_function,
154			n_iter,
155			max_time=None,
156			max_score=None,
157			early_stopping=None,
158			memory=True,
159			memory_warm_start=None,
160			verbosity=["progress_bar", "print_results", "print_times"],
161			random_state=None,
162			nth_process=None,
163			):
164			self.start_time = time.time()
165
166			if verbosity is False:
167			verbosity = []
168
169			self.objective_function = objective_function
170			self.n_iter = n_iter
171			self.max_time = max_time
172			self.max_score = max_score
173			self.early_stopping = early_stopping
174			self.memory = memory
175			self.memory_warm_start = memory_warm_start
176			self.verbosity = verbosity
177			self.random_state = random_state
178			self.nth_process = nth_process
179
180			self._init_search()
181
182			# get init positions
183			init = Initializer(self.conv)
184			self.init_positions = init.set_pos(self.initialize)
185
186			if isinstance(memory, DictProxy):
187			mem = Memory(memory_warm_start, self.conv, dict_proxy=memory)
188			self.score = self.results_mang.score(mem.memory(objective_function))
189			elif memory is True:
190			mem = Memory(memory_warm_start, self.conv)
191			self.score = self.results_mang.score(mem.memory(objective_function))
192			else:
193			self.score = self.results_mang.score(objective_function)
194
195			# loop to initialize N positions
196			for init_pos, nth_iter in zip(self.init_positions, range(n_iter)):
197			self._early_stop()
198			if self.stop:
199			break
200			self._initialization(init_pos, nth_iter)
201
202			self.finish_initialization()
203
204			# loop to do the iterations
205			for nth_iter in range(len(self.init_positions), n_iter):
206			self._early_stop()
207			if self.stop:
208			break
209			self._iteration(nth_iter)
210
211			self.results = pd.DataFrame(self.results_mang.results_list)
212
213			self.best_score = self.p_bar.score_best
214			self.best_value = self.conv.position2value(self.p_bar.pos_best)
215			self.best_para = self.conv.value2para(self.best_value)
216
217			self.results["eval_time"] = self.eval_times
218			self.results["iter_time"] = self.iter_times
219
220			if memory is not False:
221			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...
222			else:
223			self.memory_dict = {}
224
225			self.p_bar.close()
226
227			self.print_info(
228			verbosity,
229			self.objective_function,
230			self.best_score,
231			self.best_para,
232			self.eval_times,
233			self.iter_times,
234			self.n_iter,
235			)
236

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 97c085...baa350 )

Search._iteration() A

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