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import os |
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import glob |
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import numpy as np |
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import pandas as pd |
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from tqdm import tqdm |
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import matplotlib.pyplot as plt |
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from gradient_free_optimizers.converter import Converter |
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def plot_search_paths( |
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opt_name, |
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optimizer, |
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opt_para, |
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n_iter_list, |
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objective_function, |
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objective_function_np, |
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search_space, |
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initialize, |
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random_state, |
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): |
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for n_iter in tqdm(n_iter_list): |
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opt = optimizer(search_space, **opt_para) |
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opt.search( |
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objective_function, |
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n_iter=n_iter, |
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random_state=random_state, |
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memory=False, |
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verbosity=False, |
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initialize=initialize, |
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) |
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conv = Converter(search_space) |
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plt.figure(figsize=(10, 8)) |
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plt.set_cmap("jet_r") |
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x_all, y_all = search_space["x"], search_space["y"] |
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xi, yi = np.meshgrid(x_all, y_all) |
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zi = objective_function_np(xi, yi) |
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plt.imshow( |
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zi, |
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alpha=0.15, |
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# vmin=z.min(), |
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# vmax=z.max(), |
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# origin="lower", |
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extent=[x_all.min(), x_all.max(), y_all.min(), y_all.max()], |
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) |
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# print("\n Results \n", opt.results) |
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for n, opt_ in enumerate(opt.optimizers): |
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pos_list = np.array(opt_.pos_new_list) |
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score_list = np.array(opt_.score_new_list) |
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values_list = conv.positions2values(pos_list) |
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values_list = np.array(values_list) |
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plt.plot( |
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values_list[:, 0], |
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values_list[:, 1], |
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linestyle="--", |
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marker=",", |
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color="black", |
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alpha=0.33, |
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label=n, |
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linewidth=0.5, |
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) |
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plt.scatter( |
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values_list[:, 0], |
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values_list[:, 1], |
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c=score_list, |
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marker="H", |
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s=15, |
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vmin=-20000, |
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vmax=0, |
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label=n, |
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edgecolors="black", |
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linewidth=0.3, |
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) |
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plt.xlabel("x") |
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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)) |
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plt.ylim((-101, 101)) |
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plt.colorbar() |
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# plt.legend(loc="upper left", bbox_to_anchor=(-0.10, 1.2)) |
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plt.tight_layout() |
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plt.savefig( |
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"./_plots/" |
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+ str(opt.__class__.__name__) |
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+ "_" |
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+ "{0:0=3d}".format(n_iter) |
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+ ".jpg", |
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dpi=300, |
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) |
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plt.close() |
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def create_search_path_gif( |
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opt_name, |
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optimizer, |
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opt_para, |
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n_iter, |
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objective_function, |
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objective_function_np, |
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search_space, |
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): |
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pass |
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######################################################################## |
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from gradient_free_optimizers import HillClimbingOptimizer |
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optimizer_keys = ["HillClimbingOptimizer"] |
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n_iter_list = range(1, 51) |
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def get_path(optimizer_key, nth_iteration): |
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return ( |
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"./_plots/" |
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+ str(optimizer_key) |
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+ "_" |
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+ "{0:0=2d}".format(nth_iteration) |
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+ ".jpg" |
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) |
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139
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140
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def objective_function(pos_new): |
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score = -(pos_new["x"] * pos_new["x"] + pos_new["y"] * pos_new["y"]) |
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return score |
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144
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145
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def objective_function_np(x1, x2): |
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score = -(x1 * x1 + x2 * x2) |
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return score |
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149
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150
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search_space = {"x": np.arange(-100, 101, 1), "y": np.arange(-100, 101, 1)} |
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152
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n_iter_list = range(1, 3) |
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opt_para = {} |
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initialize = {"vertices": 2} |
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random_state = 0 |
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para_dict = ( |
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"hill_climbing.gif", |
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{ |
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"opt_name": "Hill climbing", |
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"optimizer": HillClimbingOptimizer, |
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"opt_para": opt_para, |
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"n_iter_list": n_iter_list, |
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"objective_function": objective_function, |
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"objective_function_np": objective_function_np, |
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"search_space": search_space, |
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"initialize": initialize, |
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"random_state": random_state, |
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}, |
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170
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) |
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172
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173
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for _para_dict in tqdm([para_dict]): |
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plot_search_paths(**_para_dict[1]) |
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_framerate = " -framerate 3 " |
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_input = " -i ./_plots/HillClimbingOptimizer_%03d.jpg " |
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_scale = " -vf scale=1200:-1 " |
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_output = " ./_gifs/" + _para_dict[0] |
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ffmpeg_command = "ffmpeg -y" + _framerate + _input + _scale + _output |
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print("\n\n -----> ffmpeg_command \n", ffmpeg_command, "\n\n") |
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print(_para_dict[0]) |
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185
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os.system(ffmpeg_command) |
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187
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rm_files = glob.glob("./_plots/*.jpg") |
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189
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for f in rm_files: |
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os.remove(f) |
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SimonBlanke /
Gradient-Free-Optimizers
