search_path_gif.search_path_gif() - Code Metrics - Inspection of "add scripts to create search-path-gifs" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( 530393...1bac85 )

by Simon

created 2022-10-29 11:58 UTC

search_path_gif.search_path_gif() B

↳ Parent: search_path_gif

Complexity

Conditions

Size

Total Lines	58
Code Lines	45

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	45
nop	10
dl	0
loc	58
rs	8.8
c	0
b	0
f	0

How to fix Long Method Many Parameters

def warn(*args, **kwargs):
    pass


import warnings

warnings.warn = warn

import os
import gc
import glob

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

import matplotlib as mpl
import matplotlib.pyplot as plt

plt.rcParams["figure.facecolor"] = "w"

mpl.use("agg")

from gradient_free_optimizers.optimizers.core_optimizer.converter import Converter


def plot_search_paths(
    path,
    optimizer,
    opt_para,
    n_iter_max,
    objective_function,
    search_space,
    initialize,
    random_state,
    title,
):
    if opt_para == {}:
        show_opt_para = False
    else:
        show_opt_para = True

    opt = optimizer(
        search_space, initialize=initialize, random_state=random_state, **opt_para
    )
    opt.search(
        objective_function,
        n_iter=n_iter_max,
        # memory=False,
        verbosity=False,
    )

    conv = Converter(search_space)
    for n_iter in tqdm(range(1, n_iter_max + 1)):

        def objective_function_np(args):
            params = {}
            for i, para_name in enumerate(search_space):
                params[para_name] = args[i]

            return objective_function(params)

        plt.figure(figsize=(7, 7))
        plt.set_cmap("jet_r")
        # jet_r

        x_all, y_all = search_space["x0"], search_space["x1"]
        xi, yi = np.meshgrid(x_all, y_all)
        zi = objective_function_np((xi, yi))

        zi = np.rot90(zi, k=1)

        plt.imshow(
            zi,
            alpha=0.15,
            # interpolation="antialiased",
            # vmin=z.min(),
            # vmax=z.max(),
            # origin="lower",
            extent=[x_all.min(), x_all.max(), y_all.min(), y_all.max()],
        )

        for n, opt_ in enumerate(opt.optimizers):
            n_optimizers = len(opt.optimizers)
            n_iter_tmp = int(n_iter / n_optimizers)
            n_iter_mod = n_iter % n_optimizers

            if n_iter_mod > n:
                n_iter_tmp += 1
            if n_iter_tmp == 0:
                continue

            pos_list = np.array(opt_.pos_new_list)
            score_list = np.array(opt_.score_new_list)

            # print("\n pos_list \n", pos_list, "\n")
            # print("\n score_list \n", score_list, "\n")

            if len(pos_list) == 0:
                continue

            values_list = conv.positions2values(pos_list)
            values_list = np.array(values_list)

            plt.plot(
                values_list[:n_iter_tmp, 0],
                values_list[:n_iter_tmp, 1],
                linestyle="--",
                marker=",",
                color="black",
                alpha=0.33,
                label=n,
                linewidth=0.5,
            )
            plt.scatter(
                values_list[:n_iter_tmp, 0],
                values_list[:n_iter_tmp, 1],
                c=score_list[:n_iter_tmp],
                marker="H",
                s=15,
                vmin=np.amin(score_list[:n_iter_tmp]),
                vmax=np.amax(score_list[:n_iter_tmp]),
                label=n,
                edgecolors="black",
                linewidth=0.3,
            )

        plt.xlabel("x")
        plt.ylabel("y")

        nth_iteration = "\n\nnth Iteration: " + str(n_iter)
        opt_para_name = ""
        opt_para_value = "\n\n"

        if show_opt_para:
            opt_para_name += "\n Parameter:"
            for para_name, para_value in opt_para.items():
                opt_para_name += "\n " + "     " + para_name + ": "
                opt_para_value += "\n " + str(para_value) + "                "

        if title == True:
            title_name = opt.name + "\n" + opt_para_name
            plt.title(title_name, loc="left")
            plt.title(opt_para_value, loc="center")
        elif isinstance(title, str):
            plt.title(title, loc="left")

        plt.title(nth_iteration, loc="right", fontsize=8)

        # plt.xlim((-101, 201))
        # plt.ylim((-101, 201))
        clb = plt.colorbar()
        clb.set_label("score", labelpad=-50, y=1.03, rotation=0)

        # plt.legend(loc="upper left", bbox_to_anchor=(-0.10, 1.2))

        # plt.axis("off")

        if show_opt_para:
            plt.subplots_adjust(top=0.75)
        plt.tight_layout()

        # plt.margins(0, 0)
        plt.savefig(
            path + "/_plots/" + opt._name_ + "_" + "{0:0=3d}".format(n_iter) + ".jpg",
            dpi=150,
            pad_inches=0,
            # bbox_inches="tight",
        )

        plt.ioff()
        # Clear the current axes.
        plt.cla()
        # Clear the current figure.
        plt.clf()
        # Closes all the figure windows.
        plt.close("all")

        gc.collect()


def search_path_gif(
    path,
    optimizer,
    opt_para,
    name,
    n_iter,
    objective_function,
    search_space,
    initialize,
    random_state=0,
    title=True,
):
    path = os.path.join(os.getcwd(), path)

    print("\n\nname", name)
    plots_dir = path + "/_plots/"
    print("plots_dir", plots_dir)
    os.makedirs(plots_dir, exist_ok=True)

    plot_search_paths(
        path=path,
        optimizer=optimizer,
        opt_para=opt_para,
        n_iter_max=n_iter,
        objective_function=objective_function,
        search_space=search_space,
        initialize=initialize,
        random_state=random_state,
        title=title,
    )

    ### ffmpeg
    framerate = str(n_iter / 10)
    # framerate = str(10)
    _framerate = " -framerate " + framerate + " "

    _opt_ = optimizer(search_space)
    _input = " -i " + path + "/_plots/" + str(_opt_._name_) + "_" + "%03d.jpg "
    _scale = " -vf scale=1200:-1:flags=lanczos "
    _output = os.path.join(path, name)

    ffmpeg_command = (
        "ffmpeg -hide_banner -loglevel error -y"
        + _framerate
        + _input
        + _scale
        + _output
    )
    print("\n -----> ffmpeg_command \n", ffmpeg_command, "\n")
    print("create " + name)

    os.system(ffmpeg_command)

    ### remove _plots
    rm_files = glob.glob(path + "/_plots/*.jpg")
    for f in rm_files:
        os.remove(f)
    os.rmdir(plots_dir)


1			def warn(args, *kwargs):
2			pass
3
4
5			import warnings
6
7			warnings.warn = warn
8
9			import os
10			import gc
11			import glob
12
13			import numpy as np
14			import pandas as pd
15			from tqdm import tqdm
16
17			import matplotlib as mpl
18			import matplotlib.pyplot as plt
19
20			plt.rcParams["figure.facecolor"] = "w"
21
22			mpl.use("agg")
23
24			from gradient_free_optimizers.optimizers.core_optimizer.converter import Converter
25
26
27			def plot_search_paths(
28			path,
29			optimizer,
30			opt_para,
31			n_iter_max,
32			objective_function,
33			search_space,
34			initialize,
35			random_state,
36			title,
37			):
38			if opt_para == {}:
39			show_opt_para = False
40			else:
41			show_opt_para = True
42
43			opt = optimizer(
44			search_space, initialize=initialize, random_state=random_state, **opt_para
45			)
46			opt.search(
47			objective_function,
48			n_iter=n_iter_max,
49			# memory=False,
50			verbosity=False,
51			)
52
53			conv = Converter(search_space)
54			for n_iter in tqdm(range(1, n_iter_max + 1)):
55
56			def objective_function_np(args):
57			params = {}
58			for i, para_name in enumerate(search_space):
59			params[para_name] = args[i]
60
61			return objective_function(params)
62
63			plt.figure(figsize=(7, 7))
64			plt.set_cmap("jet_r")
65			# jet_r
66
67			x_all, y_all = search_space["x0"], search_space["x1"]
68			xi, yi = np.meshgrid(x_all, y_all)
69			zi = objective_function_np((xi, yi))
70
71			zi = np.rot90(zi, k=1)
72
73			plt.imshow(
74			zi,
75			alpha=0.15,
76			# interpolation="antialiased",
77			# vmin=z.min(),
78			# vmax=z.max(),
79			# origin="lower",
80			extent=[x_all.min(), x_all.max(), y_all.min(), y_all.max()],
81			)
82
83			for n, opt_ in enumerate(opt.optimizers):
84			n_optimizers = len(opt.optimizers)
85			n_iter_tmp = int(n_iter / n_optimizers)
86			n_iter_mod = n_iter % n_optimizers
87
88			if n_iter_mod > n:
89			n_iter_tmp += 1
90			if n_iter_tmp == 0:
91			continue
92
93			pos_list = np.array(opt_.pos_new_list)
94			score_list = np.array(opt_.score_new_list)
95
96			# print("\n pos_list \n", pos_list, "\n")
97			# print("\n score_list \n", score_list, "\n")
98
99			if len(pos_list) == 0:
100			continue
101
102			values_list = conv.positions2values(pos_list)
103			values_list = np.array(values_list)
104
105			plt.plot(
106			values_list[:n_iter_tmp, 0],
107			values_list[:n_iter_tmp, 1],
108			linestyle="--",
109			marker=",",
110			color="black",
111			alpha=0.33,
112			label=n,
113			linewidth=0.5,
114			)
115			plt.scatter(
116			values_list[:n_iter_tmp, 0],
117			values_list[:n_iter_tmp, 1],
118			c=score_list[:n_iter_tmp],
119			marker="H",
120			s=15,
121			vmin=np.amin(score_list[:n_iter_tmp]),
122			vmax=np.amax(score_list[:n_iter_tmp]),
123			label=n,
124			edgecolors="black",
125			linewidth=0.3,
126			)
127
128			plt.xlabel("x")
129			plt.ylabel("y")
130
131			nth_iteration = "\n\nnth Iteration: " + str(n_iter)
132			opt_para_name = ""
133			opt_para_value = "\n\n"
134
135			if show_opt_para:
136			opt_para_name += "\n Parameter:"
137			for para_name, para_value in opt_para.items():
138			opt_para_name += "\n " + " " + para_name + ": "
139			opt_para_value += "\n " + str(para_value) + " "
140
141			if title == True:
142			title_name = opt.name + "\n" + opt_para_name
143			plt.title(title_name, loc="left")
144			plt.title(opt_para_value, loc="center")
145			elif isinstance(title, str):
146			plt.title(title, loc="left")
147
148			plt.title(nth_iteration, loc="right", fontsize=8)
149
150			# plt.xlim((-101, 201))
151			# plt.ylim((-101, 201))
152			clb = plt.colorbar()
153			clb.set_label("score", labelpad=-50, y=1.03, rotation=0)
154
155			# plt.legend(loc="upper left", bbox_to_anchor=(-0.10, 1.2))
156
157			# plt.axis("off")
158
159			if show_opt_para:
160			plt.subplots_adjust(top=0.75)
161			plt.tight_layout()
162
163			# plt.margins(0, 0)
164			plt.savefig(
165			path + "/_plots/" + opt._name_ + "_" + "{0:0=3d}".format(n_iter) + ".jpg",
166			dpi=150,
167			pad_inches=0,
168			# bbox_inches="tight",
169			)
170
171			plt.ioff()
172			# Clear the current axes.
173			plt.cla()
174			# Clear the current figure.
175			plt.clf()
176			# Closes all the figure windows.
177			plt.close("all")
178
179			gc.collect()
180
181
182			def search_path_gif(
183			path,
184			optimizer,
185			opt_para,
186			name,
187			n_iter,
188			objective_function,
189			search_space,
190			initialize,
191			random_state=0,
192			title=True,
193			):
194			path = os.path.join(os.getcwd(), path)
195
196			print("\n\nname", name)
197			plots_dir = path + "/_plots/"
198			print("plots_dir", plots_dir)
199			os.makedirs(plots_dir, exist_ok=True)
200
201			plot_search_paths(
202			path=path,
203			optimizer=optimizer,
204			opt_para=opt_para,
205			n_iter_max=n_iter,
206			objective_function=objective_function,
207			search_space=search_space,
208			initialize=initialize,
209			random_state=random_state,
210			title=title,
211			)
212
213			### ffmpeg
214			framerate = str(n_iter / 10)
215			# framerate = str(10)
216			_framerate = " -framerate " + framerate + " "
217
218			_opt_ = optimizer(search_space)
219			_input = " -i " + path + "/_plots/" + str(_opt_._name_) + "_" + "%03d.jpg "
220			_scale = " -vf scale=1200:-1:flags=lanczos "
221			_output = os.path.join(path, name)
222
223			ffmpeg_command = (
224			"ffmpeg -hide_banner -loglevel error -y"
225			+ _framerate
226			+ _input
227			+ _scale
228			+ _output
229			)
230			print("\n -----> ffmpeg_command \n", ffmpeg_command, "\n")
231			print("create " + name)
232
233			os.system(ffmpeg_command)
234
235			### remove _plots
236			rm_files = glob.glob(path + "/_plots/*.jpg")
237			for f in rm_files:
238			os.remove(f)
239			os.rmdir(plots_dir)
240

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 530393...1bac85 )

search_path_gif.search_path_gif() B

Complexity

Size

Duplication

Importance

How to fix Long Method Many Parameters

Long Method

Many Parameters

Duplication Side-by-Side

Filter issues like