Python库 | scikit-opt-0.5.4.tar.gz

<table border="0" width="10%"> <tr> <td><img src="https://github.com/guofei9987/pictures_for_blog/blob/master/tmp/1.jpg?raw=true" height="80" width="82"></td> <td><img src="https://github.com/guofei9987/pictures_for_blog/blob/master/tmp/2.jpg?raw=true" height="80" width="82"></td> <td><img src="https://github.com/guofei9987/pictures_for_blog/blob/master/tmp/3.jpg?raw=true" height="80" width="82"></td> </tr> <tr> <td><img src="https://github.com/guofei9987/pictures_for_blog/blob/master/tmp/4.jpg?raw=true" height="80" width="82"></td> <td><img src="https://img.shields.io/github/stars/guofei9987/scikit-opt.svg?style=social"></td> <td><img src="https://github.com/guofei9987/pictures_for_blog/blob/master/tmp/6.jpg?raw=true" height="82" width="82"></td> </tr> <tr> <td><img src="https://github.com/guofei9987/pictures_for_blog/blob/master/tmp/7.jpg?raw=true" height="82" width="82"></td> <td><img src="https://github.com/guofei9987/pictures_for_blog/blob/master/tmp/8.jpg?raw=true" height="82" width="82"></td> <td><img src="https://github.com/guofei9987/pictures_for_blog/blob/master/tmp/9.jpg?raw=true" height="82" width="82"></td> </tr> </table> # [scikit-opt](https://github.com/guofei9987/scikit-opt) [](https://pypi.org/project/scikit-opt/) [](https://travis-ci.com/guofei9987/scikit-opt) [](https://codecov.io/gh/guofei9987/scikit-opt) [](https://github.com/guofei9987/scikit-opt/blob/master/LICENSE)   [](https://pypi.org/project/scikit-opt/) [](https://gitter.im/guofei9987/scikit-opt?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,Artificial Fish Swarm Algorithm in Python) - **Documentation:** [https://scikit-opt.github.io/scikit-opt/#/en/](https://scikit-opt.github.io/scikit-opt/#/en/) - **文档：** [https://scikit-opt.github.io/scikit-opt/#/zh/](https://scikit-opt.github.io/scikit-opt/#/zh/) - **Source code:** [https://github.com/guofei9987/scikit-opt](https://github.com/guofei9987/scikit-opt) - **Help us improve scikit-opt** [https://www.wjx.cn/jq/50964691.aspx](https://www.wjx.cn/jq/50964691.aspx) # install ```bash pip install scikit-opt ``` For the current developer version: ```bach git clone git@github.com:guofei9987/scikit-opt.git cd scikit-opt pip install . ``` ## News: All algorithms will be available on ~~/Spark/Pytorch~~ **TensorFlow** on version ~~0.4~~ **1.x**, getting parallel performance. Have fun! ### feature1: UDF **UDF** (user defined function) is available now! For example, you just worked out a new type of `selection` function. Now, your `selection` function is like this: -> Demo code: [examples/demo_ga_udf.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L1) ```python # step1: define your own operator: def selection_tournament(self, tourn_size): FitV = self.FitV sel_index = [] for i in range(self.size_pop): aspirants_index = np.random.choice(range(self.size_pop), size=tourn_size) sel_index.append(max(aspirants_index, key=lambda i: FitV[i])) self.Chrom = self.Chrom[sel_index, :] # next generation return self.Chrom ``` Import and build ga -> Demo code: [examples/demo_ga_udf.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L12) ```python import numpy as np from sko.GA import GA, GA_TSP demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2 ga = GA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -5], ub=[2, 10, 2], precision=[1e-7, 1e-7, 1]) ``` Regist your udf to GA -> Demo code: [examples/demo_ga_udf.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L20) ```python ga.register(operator_name='selection', operator=selection_tournament, tourn_size=3) ``` scikit-opt also provide some operators -> Demo code: [examples/demo_ga_udf.py#s4](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L22) ```python from sko.operators import ranking, selection, crossover, mutation ga.register(operator_name='ranking', operator=ranking.ranking). \ register(operator_name='crossover', operator=crossover.crossover_2point). \ register(operator_name='mutation', operator=mutation.mutation) ``` Now do GA as usual -> Demo code: [examples/demo_ga_udf.py#s5](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L29) ```python best_x, best_y = ga.run() print('best_x:', best_x, '\n', 'best_y:', best_y) ``` > Until Now, the **udf** surport `crossover`, `mutation`, `selection`, `ranking` of GA > scikit-opt provide a dozen of operators, see [here](https://github.com/guofei9987/scikit-opt/tree/master/sko/operators) ### feature2: continue to run (New in version 0.3.6) Run an algorithm for 10 iterations, and then run another 20 iterations base on the 10 iterations before: ```python from sko.GA import GA func = lambda x: x[0] ** 2 ga = GA(func=func, n_dim=1) ga.run(10) ga.run(20) ``` # Quick start ## 1. Differential Evolution **Step1**：define your problem -> Demo code: [examples/demo_de.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L1) ```python ''' min f(x1, x2, x3) = x1^2 + x2^2 + x3^2 s.t. x1*x2 >= 1 x1*x2 <= 5 x2 + x3 = 1 0 <= x1, x2, x3 <= 5 ''' def obj_func(p): x1, x2, x3 = p return x1 ** 2 + x2 ** 2 + x3 ** 2 constraint_eq = [ lambda x: 1 - x[1] - x[2] ] constraint_ueq = [ lambda x: 1 - x[0] * x[1], lambda x: x[0] * x[1] - 5 ] ``` **Step2**: do Differential Evolution -> Demo code: [examples/demo_de.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L25) ```python from sko.DE import DE de = DE(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0], ub=[5, 5, 5], constraint_eq=constraint_eq, constraint_ueq=constraint_ueq) best_x, best_y = de.run() print('best_x:', best_x, '\n', 'best_y:', best_y) ``` ## 2. Genetic Algorithm **Step1**：define your problem -> Demo code: [examples/demo_ga.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L1) ```python import numpy as np def schaffer(p): ''' This function has plenty of local minimum, with strong shocks global minimum at (0,0) with value 0 ''' x1, x2 = p x = np.square(x1) + np.square(x2) return 0.5 + (np.sin(x) - 0.5) / np.square(1 + 0.001 * x) ``` **Step2**: do Genetic Algorithm -> Demo code: [examples/demo_ga.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L14) ```python from sko.GA import GA ga = GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, lb=[-1, -1], ub=[1, 1], precision=1e-7) best_x, best_y = ga.run() print('best_x:', best_x, '\n', 'best_y:', best_y) ``` **Step3**: plot the result -> Demo code: [examples/demo_ga.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L21) ```python import pandas as pd import matplotlib.pyplot as plt Y_history = pd.DataFrame(ga.all_h