create_performance_data.create_performance_data() - Code Metrics - Inspection of "add png + api information" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 13200e...4b50d2 )

by Simon

created 2020-12-08 10:43 UTC

create_performance_data.create_performance_data() B

↳ Parent: create_performance_data

Complexity

Conditions

Size

Total Lines	61
Code Lines	46

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	3
eloc	46
nop	4
dl	0
loc	61
rs	8.7672
c	0
b	0
f	0

How to fix Long Method

import time
from tqdm import tqdm
import numpy as np
import pandas as pd

from gradient_free_optimizers import (
    HillClimbingOptimizer,
    StochasticHillClimbingOptimizer,
    TabuOptimizer,
    RandomSearchOptimizer,
    RandomRestartHillClimbingOptimizer,
    RandomAnnealingOptimizer,
    SimulatedAnnealingOptimizer,
    ParallelTemperingOptimizer,
    ParticleSwarmOptimizer,
    EvolutionStrategyOptimizer,
    BayesianOptimizer,
    TreeStructuredParzenEstimators,
    DecisionTreeOptimizer,
    EnsembleOptimizer,
)


n_inits = 4

optimizer_dict = {
    "Hill climbing": HillClimbingOptimizer,
    "Stochastic hill climbing": StochasticHillClimbingOptimizer,
    "Tabu search": TabuOptimizer,
    "Random search": RandomSearchOptimizer,
    "Random restart hill climbing": RandomRestartHillClimbingOptimizer,
    "Random annealing": RandomAnnealingOptimizer,
    "Simulated annealing": SimulatedAnnealingOptimizer,
    "Parallel tempering": ParallelTemperingOptimizer,
    "Particle swarm optimizer": ParticleSwarmOptimizer,
    "Evolution strategy": EvolutionStrategyOptimizer,
    # "Bayesian optimizer": BayesianOptimizer,
    # "Tree structured parzen estimators": TreeStructuredParzenEstimators,
    # "Decision tree optimizer": DecisionTreeOptimizer,
    # "Ensemble optimizer": EnsembleOptimizer,
}


def objective_function(pos_new):
    score = -(pos_new["x1"] * pos_new["x1"] + pos_new["x2"] * pos_new["x2"])
    return score


search_space = {"x1": np.arange(-10, 11, 0.1), "x2": np.arange(-10, 11, 0.1)}

runs = 30


def create_performance_data(
    study_name, objective_function, search_space, n_iter
):
    results = []

    for opt_name in tqdm(optimizer_dict.keys()):
        total_time_list = []
        eval_time_list = []
        iter_time_list = []

        for random_state in tqdm(range(runs)):

            c_time = time.time()
            opt = optimizer_dict[opt_name](search_space)
            opt.search(
                objective_function,
                n_iter=n_iter,
                verbosity=False,
                random_state=random_state,
            )

            total_time = time.time() - c_time
            eval_time = np.array(opt.eval_times).sum()
            iter_time = np.array(opt.iter_times).sum()

            total_time_list.append(total_time)
            eval_time_list.append(eval_time)
            iter_time_list.append(iter_time)

        total_time_mean = np.array(total_time_list).mean()
        eval_time_mean = np.array(eval_time_list).mean()
        iter_time_mean = np.array(iter_time_list).mean()

        total_time_std = np.array(total_time_list).std()
        eval_time_std = np.array(eval_time_list).std()
        iter_time_std = np.array(iter_time_list).std()

        results.append(
            [
                total_time_mean,
                total_time_std,
                eval_time_mean,
                eval_time_std,
                iter_time_mean,
                iter_time_std,
            ]
        )

    index = [
        "total_time_mean",
        "total_time_std",
        "eval_time_mean",
        "eval_time_std",
        "iter_time_mean",
        "iter_time_std",
    ]
    columns = list(optimizer_dict.keys())

    results = np.array(results).T
    results = pd.DataFrame(results, columns=columns, index=index)
    results.to_csv("./_data/" + study_name + ".csv")


create_performance_data(
    "simple function", objective_function, search_space, n_iter=50
)



1			import time
2			from tqdm import tqdm
3			import numpy as np
4			import pandas as pd
5
6			from gradient_free_optimizers import (
7			HillClimbingOptimizer,
8			StochasticHillClimbingOptimizer,
9			TabuOptimizer,
10			RandomSearchOptimizer,
11			RandomRestartHillClimbingOptimizer,
12			RandomAnnealingOptimizer,
13			SimulatedAnnealingOptimizer,
14			ParallelTemperingOptimizer,
15			ParticleSwarmOptimizer,
16			EvolutionStrategyOptimizer,
17			BayesianOptimizer,
18			TreeStructuredParzenEstimators,
19			DecisionTreeOptimizer,
20			EnsembleOptimizer,
21			)
22
23
24			n_inits = 4
25
26			optimizer_dict = {
27			"Hill climbing": HillClimbingOptimizer,
28			"Stochastic hill climbing": StochasticHillClimbingOptimizer,
29			"Tabu search": TabuOptimizer,
30			"Random search": RandomSearchOptimizer,
31			"Random restart hill climbing": RandomRestartHillClimbingOptimizer,
32			"Random annealing": RandomAnnealingOptimizer,
33			"Simulated annealing": SimulatedAnnealingOptimizer,
34			"Parallel tempering": ParallelTemperingOptimizer,
35			"Particle swarm optimizer": ParticleSwarmOptimizer,
36			"Evolution strategy": EvolutionStrategyOptimizer,
37			# "Bayesian optimizer": BayesianOptimizer,
38			# "Tree structured parzen estimators": TreeStructuredParzenEstimators,
39			# "Decision tree optimizer": DecisionTreeOptimizer,
40			# "Ensemble optimizer": EnsembleOptimizer,
41			}
42
43
44			def objective_function(pos_new):
45			score = -(pos_new["x1"] * pos_new["x1"] + pos_new["x2"] * pos_new["x2"])
46			return score
47
48
49			search_space = {"x1": np.arange(-10, 11, 0.1), "x2": np.arange(-10, 11, 0.1)}
50
51			runs = 30
52
53
54			def create_performance_data(
55			study_name, objective_function, search_space, n_iter
56			):
57			results = []
58
59			for opt_name in tqdm(optimizer_dict.keys()):
60			total_time_list = []
61			eval_time_list = []
62			iter_time_list = []
63
64			for random_state in tqdm(range(runs)):
65
66			c_time = time.time()
67			opt = optimizer_dict[opt_name](search_space)
68			opt.search(
69			objective_function,
70			n_iter=n_iter,
71			verbosity=False,
72			random_state=random_state,
73			)
74
75			total_time = time.time() - c_time
76			eval_time = np.array(opt.eval_times).sum()
77			iter_time = np.array(opt.iter_times).sum()
78
79			total_time_list.append(total_time)
80			eval_time_list.append(eval_time)
81			iter_time_list.append(iter_time)
82
83			total_time_mean = np.array(total_time_list).mean()
84			eval_time_mean = np.array(eval_time_list).mean()
85			iter_time_mean = np.array(iter_time_list).mean()
86
87			total_time_std = np.array(total_time_list).std()
88			eval_time_std = np.array(eval_time_list).std()
89			iter_time_std = np.array(iter_time_list).std()
90
91			results.append(
92			[
93			total_time_mean,
94			total_time_std,
95			eval_time_mean,
96			eval_time_std,
97			iter_time_mean,
98			iter_time_std,
99			]
100			)
101
102			index = [
103			"total_time_mean",
104			"total_time_std",
105			"eval_time_mean",
106			"eval_time_std",
107			"iter_time_mean",
108			"iter_time_std",
109			]
110			columns = list(optimizer_dict.keys())
111
112			results = np.array(results).T
113			results = pd.DataFrame(results, columns=columns, index=index)
114			results.to_csv("./_data/" + study_name + ".csv")
115
116
117			create_performance_data(
118			"simple function", objective_function, search_space, n_iter=50
119			)
120
121

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 13200e...4b50d2 )

create_performance_data.create_performance_data() B

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