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add script to create search path gifs
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@@ -28,6 +28,7 @@ share/python-wheels/ | |
MANIFEST | ||
**/_plots/ | ||
**/_data/ | ||
**/_gifs/ | ||
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# Dev | ||
*___.* | ||
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import os | ||
import glob | ||
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import numpy as np | ||
import pandas as pd | ||
from tqdm import tqdm | ||
import matplotlib.pyplot as plt | ||
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from gradient_free_optimizers.converter import Converter | ||
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def plot_search_paths( | ||
opt_name, | ||
optimizer, | ||
opt_para, | ||
n_iter_list, | ||
objective_function, | ||
objective_function_np, | ||
search_space, | ||
initialize, | ||
random_state, | ||
): | ||
for n_iter in tqdm(n_iter_list): | ||
opt = optimizer(search_space, **opt_para) | ||
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opt.search( | ||
objective_function, | ||
n_iter=n_iter, | ||
random_state=random_state, | ||
memory=False, | ||
verbosity=False, | ||
initialize=initialize, | ||
) | ||
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conv = Converter(search_space) | ||
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plt.figure(figsize=(10, 8)) | ||
plt.set_cmap("jet_r") | ||
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x_all, y_all = search_space["x"], search_space["y"] | ||
xi, yi = np.meshgrid(x_all, y_all) | ||
zi = objective_function_np(xi, yi) | ||
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plt.imshow( | ||
zi, | ||
alpha=0.15, | ||
# vmin=z.min(), | ||
# vmax=z.max(), | ||
# origin="lower", | ||
extent=[x_all.min(), x_all.max(), y_all.min(), y_all.max()], | ||
) | ||
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# print("\n Results \n", opt.results) | ||
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for n, opt_ in enumerate(opt.optimizers): | ||
pos_list = np.array(opt_.pos_new_list) | ||
score_list = np.array(opt_.score_new_list) | ||
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values_list = conv.positions2values(pos_list) | ||
values_list = np.array(values_list) | ||
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plt.plot( | ||
values_list[:, 0], | ||
values_list[:, 1], | ||
linestyle="--", | ||
marker=",", | ||
color="black", | ||
alpha=0.33, | ||
label=n, | ||
linewidth=0.5, | ||
) | ||
plt.scatter( | ||
values_list[:, 0], | ||
values_list[:, 1], | ||
c=score_list, | ||
marker="H", | ||
s=15, | ||
vmin=-20000, | ||
vmax=0, | ||
label=n, | ||
edgecolors="black", | ||
linewidth=0.3, | ||
) | ||
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plt.xlabel("x") | ||
plt.ylabel("y") | ||
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nth_iteration = "\n\nnth Iteration: " + str(n_iter) | ||
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plt.title(opt_name + nth_iteration) | ||
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plt.xlim((-101, 101)) | ||
plt.ylim((-101, 101)) | ||
plt.colorbar() | ||
# plt.legend(loc="upper left", bbox_to_anchor=(-0.10, 1.2)) | ||
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plt.tight_layout() | ||
plt.savefig( | ||
"./_plots/" | ||
+ str(opt.__class__.__name__) | ||
+ "_" | ||
+ "{0:0=3d}".format(n_iter) | ||
+ ".jpg", | ||
dpi=300, | ||
) | ||
plt.close() | ||
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def create_search_path_gif( | ||
opt_name, | ||
optimizer, | ||
opt_para, | ||
n_iter, | ||
objective_function, | ||
objective_function_np, | ||
search_space, | ||
): | ||
pass | ||
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######################################################################## | ||
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from gradient_free_optimizers import HillClimbingOptimizer | ||
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optimizer_keys = ["HillClimbingOptimizer"] | ||
n_iter_list = range(1, 51) | ||
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def get_path(optimizer_key, nth_iteration): | ||
return ( | ||
"./_plots/" | ||
+ str(optimizer_key) | ||
+ "_" | ||
+ "{0:0=2d}".format(nth_iteration) | ||
+ ".jpg" | ||
) | ||
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def objective_function(pos_new): | ||
score = -(pos_new["x"] * pos_new["x"] + pos_new["y"] * pos_new["y"]) | ||
return score | ||
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def objective_function_np(x1, x2): | ||
score = -(x1 * x1 + x2 * x2) | ||
return score | ||
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search_space = {"x": np.arange(-100, 101, 1), "y": np.arange(-100, 101, 1)} | ||
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n_iter_list = range(1, 3) | ||
opt_para = {} | ||
initialize = {"vertices": 2} | ||
random_state = 0 | ||
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para_dict = ( | ||
"hill_climbing.gif", | ||
{ | ||
"opt_name": "Hill climbing", | ||
"optimizer": HillClimbingOptimizer, | ||
"opt_para": opt_para, | ||
"n_iter_list": n_iter_list, | ||
"objective_function": objective_function, | ||
"objective_function_np": objective_function_np, | ||
"search_space": search_space, | ||
"initialize": initialize, | ||
"random_state": random_state, | ||
}, | ||
) | ||
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for _para_dict in tqdm([para_dict]): | ||
plot_search_paths(**_para_dict[1]) | ||
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_framerate = " -framerate 3 " | ||
_input = " -i ./_plots/HillClimbingOptimizer_%03d.jpg " | ||
_scale = " -vf scale=1200:-1 " | ||
_output = " ./_gifs/" + _para_dict[0] | ||
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ffmpeg_command = "ffmpeg -y" + _framerate + _input + _scale + _output | ||
print("\n\n -----> ffmpeg_command \n", ffmpeg_command, "\n\n") | ||
print(_para_dict[0]) | ||
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os.system(ffmpeg_command) | ||
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rm_files = glob.glob("./_plots/*.jpg") | ||
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for f in rm_files: | ||
os.remove(f) | ||
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