Metadata-Version: 2.1
Name: scikit-opt
Version: 0.6.6
Summary: Swarm Intelligence in Python
Home-page: https://github.com/guofei9987/scikit-opt
Author: Guo Fei
Author-email: guofei9987@foxmail.com
License: MIT
Description: 
        
        # [scikit-opt](https://github.com/guofei9987/scikit-opt)
        
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        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 .
        ```
        
        # Features
        ## 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(algorithm, tourn_size):
            FitV = algorithm.FitV
            sel_index = []
            for i in range(algorithm.size_pop):
                aspirants_index = np.random.choice(range(algorithm.size_pop), size=tourn_size)
                sel_index.append(max(aspirants_index, key=lambda i: FitV[i]))
            algorithm.Chrom = algorithm.Chrom[sel_index, :]  # next generation
            return algorithm.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, prob_mut=0.001,
                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#L28)
        ```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)
        
        For advanced users:
        
        -> Demo code: [examples/demo_ga_udf.py#s6](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L31)
        ```python
        class MyGA(GA):
            def selection(self, tourn_size=3):
                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
        
            ranking = ranking.ranking
        
        
        demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2
        my_ga = MyGA(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])
        best_x, best_y = my_ga.run()
        print('best_x:', best_x, '\n', 'best_y:', best_y)
        ```
        
        ##  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)
        ```
        
        ## feature3: 4-ways to accelerate
        - vectorization
        - multithreading
        - multiprocessing
        - cached
        
        see [https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py)
        
        
        
        ## feature4: GPU computation
         We are developing GPU computation, which will be stable on version 1.0.0  
        An example is already available: [https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py)
        
        
        # 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
            https://en.wikipedia.org/wiki/Test_functions_for_optimization
            '''
            x1, x2 = p
            part1 = np.square(x1) - np.square(x2)
            part2 = np.square(x1) + np.square(x2)
            return 0.5 + (np.square(np.sin(part1)) - 0.5) / np.square(1 + 0.001 * part2)
        
        
        ```
        
        **Step2**: do Genetic Algorithm  
        -> Demo code: [examples/demo_ga.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L16)
        ```python
        from sko.GA import GA
        
        ga = GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, prob_mut=0.001, 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)
        ```
        
        -> Demo code: [examples/demo_ga.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L22)
        ```python
        import pandas as pd
        import matplotlib.pyplot as plt
        
        Y_history = pd.DataFrame(ga.all_history_Y)
        fig, ax = plt.subplots(2, 1)
        ax[0].plot(Y_history.index, Y_history.values, '.', color='red')
        Y_history.min(axis=1).cummin().plot(kind='line')
        plt.show()
        ```
        
        ![Figure_1-1](https://img1.github.io/heuristic_algorithm/ga_1.png)
        
        ### 2.2 Genetic Algorithm for TSP(Travelling Salesman Problem)
        Just import the `GA_TSP`, it overloads the `crossover`, `mutation` to solve the TSP
        
        **Step1**: define your problem. Prepare your points coordinate and the distance matrix.  
        Here I generate the data randomly as a demo:  
        -> Demo code: [examples/demo_ga_tsp.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L1)
        ```python
        import numpy as np
        from scipy import spatial
        import matplotlib.pyplot as plt
        
        num_points = 50
        
        points_coordinate = np.random.rand(num_points, 2)  # generate coordinate of points
        distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean')
        
        
        def cal_total_distance(routine):
            '''The objective function. input routine, return total distance.
            cal_total_distance(np.arange(num_points))
            '''
            num_points, = routine.shape
            return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)])
        
        
        ```
        
        **Step2**: do GA  
        -> Demo code: [examples/demo_ga_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L19)
        ```python
        
        from sko.GA import GA_TSP
        
        ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=500, prob_mut=1)
        best_points, best_distance = ga_tsp.run()
        
        ```
        
        **Step3**: Plot the result:  
        -> Demo code: [examples/demo_ga_tsp.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L26)
        ```python
        fig, ax = plt.subplots(1, 2)
        best_points_ = np.concatenate([best_points, [best_points[0]]])
        best_points_coordinate = points_coordinate[best_points_, :]
        ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')
        ax[1].plot(ga_tsp.generation_best_Y)
        plt.show()
        ```
        
        ![GA_TPS](https://img1.github.io/heuristic_algorithm/ga_tsp.png)
        
        
        ## 3. PSO(Particle swarm optimization)
        
        ### 3.1 PSO
        **Step1**: define your problem:  
        -> Demo code: [examples/demo_pso.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L1)
        ```python
        def demo_func(x):
            x1, x2, x3 = x
            return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2
        
        
        ```
        
        **Step2**: do PSO  
        -> Demo code: [examples/demo_pso.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L6)
        ```python
        from sko.PSO import PSO
        
        pso = PSO(func=demo_func, n_dim=3, pop=40, max_iter=150, lb=[0, -1, 0.5], ub=[1, 1, 1], w=0.8, c1=0.5, c2=0.5)
        pso.run()
        print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)
        
        ```
        
        **Step3**: Plot the result  
        -> Demo code: [examples/demo_pso.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L13)
        ```python
        import matplotlib.pyplot as plt
        
        plt.plot(pso.gbest_y_hist)
        plt.show()
        ```
        
        
        ![PSO_TPS](https://img1.github.io/heuristic_algorithm/pso.png)
        
        ### 3.2 PSO with nonlinear constraint
        
        If you need nolinear constraint like `(x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2<=0`  
        Codes are like this:
        ```python
        constraint_ueq = (
            lambda x: (x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2
            ,
        )
        pso = PSO(func=demo_func, n_dim=2, pop=40, max_iter=max_iter, lb=[-2, -2], ub=[2, 2]
                  , constraint_ueq=constraint_ueq)
        ```
        
        Note that, you can add more then one nonlinear constraint. Just add it to `constraint_ueq`
        
        More over, we have an animation:  
        ![pso_ani](https://img1.github.io/heuristic_algorithm/pso.gif)  
        ↑**see [examples/demo_pso_ani.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py)**
        
        
        ## 4. SA(Simulated Annealing)
        ### 4.1 SA for multiple function
        **Step1**: define your problem  
        -> Demo code: [examples/demo_sa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L1)
        ```python
        demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2
        
        ```
        **Step2**: do SA  
        -> Demo code: [examples/demo_sa.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L3)
        ```python
        from sko.SA import SA
        
        sa = SA(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, L=300, max_stay_counter=150)
        best_x, best_y = sa.run()
        print('best_x:', best_x, 'best_y', best_y)
        
        ```
        
        **Step3**: Plot the result  
        -> Demo code: [examples/demo_sa.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L10)
        ```python
        import matplotlib.pyplot as plt
        import pandas as pd
        
        plt.plot(pd.DataFrame(sa.best_y_history).cummin(axis=0))
        plt.show()
        
        ```
        ![sa](https://img1.github.io/heuristic_algorithm/sa.png)
        
        
        Moreover, scikit-opt provide 3 types of Simulated Annealing: Fast, Boltzmann, Cauchy. See [more sa](https://scikit-opt.github.io/scikit-opt/#/en/more_sa)
        ### 4.2 SA for TSP
        **Step1**: oh, yes, define your problems. To boring to copy this step.  
        
        **Step2**: DO SA for TSP  
        -> Demo code: [examples/demo_sa_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py#L21)
        ```python
        from sko.SA import SA_TSP
        
        sa_tsp = SA_TSP(func=cal_total_distance, x0=range(num_points), T_max=100, T_min=1, L=10 * num_points)
        
        best_points, best_distance = sa_tsp.run()
        print(best_points, best_distance, cal_total_distance(best_points))
        ```
        
        **Step3**: plot the result  
        -> Demo code: [examples/demo_sa_tsp.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py#L28)
        ```python
        from matplotlib.ticker import FormatStrFormatter
        
        fig, ax = plt.subplots(1, 2)
        
        best_points_ = np.concatenate([best_points, [best_points[0]]])
        best_points_coordinate = points_coordinate[best_points_, :]
        ax[0].plot(sa_tsp.best_y_history)
        ax[0].set_xlabel("Iteration")
        ax[0].set_ylabel("Distance")
        ax[1].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1],
                   marker='o', markerfacecolor='b', color='c', linestyle='-')
        ax[1].xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
        ax[1].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
        ax[1].set_xlabel("Longitude")
        ax[1].set_ylabel("Latitude")
        plt.show()
        
        ```
        ![sa](https://img1.github.io/heuristic_algorithm/sa_tsp.png)
        
        
        More: Plot the animation:  
        
        ![sa](https://img1.github.io/heuristic_algorithm/sa_tsp1.gif)  
        ↑**see [examples/demo_sa_tsp.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py)**
        
        
        
        
        ## 5. ACA (Ant Colony Algorithm) for tsp
        -> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L17)
        ```python
        from sko.ACA import ACA_TSP
        
        aca = ACA_TSP(func=cal_total_distance, n_dim=num_points,
                      size_pop=50, max_iter=200,
                      distance_matrix=distance_matrix)
        
        best_x, best_y = aca.run()
        
        ```
        
        ![ACA](https://img1.github.io/heuristic_algorithm/aca_tsp.png)
        
        
        ## 6. immune algorithm (IA)
        -> Demo code: [examples/demo_ia.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ia.py#L6)
        ```python
        
        from sko.IA import IA_TSP
        
        ia_tsp = IA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=500, max_iter=800, prob_mut=0.2,
                        T=0.7, alpha=0.95)
        best_points, best_distance = ia_tsp.run()
        print('best routine:', best_points, 'best_distance:', best_distance)
        
        ```
        
        ![IA](https://img1.github.io/heuristic_algorithm/ia2.png)
        
        ## 7. Artificial Fish Swarm Algorithm (AFSA)
        -> Demo code: [examples/demo_afsa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_afsa.py#L1)
        ```python
        def func(x):
            x1, x2 = x
            return 1 / x1 ** 2 + x1 ** 2 + 1 / x2 ** 2 + x2 ** 2
        
        
        from sko.AFSA import AFSA
        
        afsa = AFSA(func, n_dim=2, size_pop=50, max_iter=300,
                    max_try_num=100, step=0.5, visual=0.3,
                    q=0.98, delta=0.5)
        best_x, best_y = afsa.run()
        print(best_x, best_y)
        ```
        
        
        # Projects using scikit-opt
        
        - [Yu, J., He, Y., Yan, Q., & Kang, X. (2021). SpecView: Malware Spectrum Visualization Framework With Singular Spectrum Transformation. IEEE Transactions on Information Forensics and Security, 16, 5093-5107.](https://ieeexplore.ieee.org/abstract/document/9607026/)
        - [Zhen, H., Zhai, H., Ma, W., Zhao, L., Weng, Y., Xu, Y., ... & He, X. (2021). Design and tests of reinforcement-learning-based optimal power flow solution generator. Energy Reports.](https://www.sciencedirect.com/science/article/pii/S2352484721012737)
        - [Heinrich, K., Zschech, P., Janiesch, C., & Bonin, M. (2021). Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decision Support Systems, 143, 113494.](https://www.sciencedirect.com/science/article/pii/S016792362100004X)
        - [Tang, H. K., & Goh, S. K. (2021). A Novel Non-population-based Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing. arXiv preprint arXiv:2104.08564.](https://arxiv.org/abs/2104.08564)
        - [Wu, G., Li, L., Li, X., Chen, Y., Chen, Z., Qiao, B., ... & Xia, L. (2021). Graph embedding based real-time social event matching for EBSNs recommendation. World Wide Web, 1-22.](https://link.springer.com/article/10.1007/s11280-021-00934-y)
        - [Pan, X., Zhang, Z., Zhang, H., Wen, Z., Ye, W., Yang, Y., ... & Zhao, X. (2021). A fast and robust mixture gases identification and concentration detection algorithm based on attention mechanism equipped recurrent neural network with double loss function. Sensors and Actuators B: Chemical, 342, 129982.](https://www.sciencedirect.com/science/article/abs/pii/S0925400521005517)
        - [Castella Balcell, M. (2021). Optimization of the station keeping system for the WindCrete floating offshore wind turbine.](https://upcommons.upc.edu/handle/2117/350262)
        - [Zhai, B., Wang, Y., Wang, W., & Wu, B. (2021). Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions. arXiv preprint arXiv:2107.14406.](https://arxiv.org/abs/2107.14406)
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Platform: windows
Platform: macos
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