diff --git a/README.md b/README.md index 9dd19793..92536b0b 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ - +from openpyxl.descriptors import Integer

MEALPY --- -[![GitHub release](https://img.shields.io/badge/release-3.0.0-yellow.svg)](https://github.com/thieu1995/mealpy/releases) +[![GitHub release](https://img.shields.io/badge/release-3.0.1-yellow.svg)](https://github.com/thieu1995/mealpy/releases) [![Wheel](https://img.shields.io/pypi/wheel/gensim.svg)](https://pypi.python.org/pypi/mealpy) [![PyPI version](https://badge.fury.io/py/mealpy.svg)](https://badge.fury.io/py/mealpy) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mealpy.svg) @@ -90,7 +90,7 @@ Please include these citations if you plan to use this library:

Goals

 -Our goals are to implement all of the classical as well as the state-of-the-art nature-inspired algorithms, create a simple interface that helps researchers access optimization algorithms as quickly as possible, and share knowledge of the optimization field with everyone without a fee. What you can do with mealpy: +Our goals are to implement all classical as well as the state-of-the-art nature-inspired algorithms, create a simple interface that helps researchers access optimization algorithms as quickly as possible, and share knowledge of the optimization field with everyone without a fee. What you can do with mealpy: - Analyse parameters of meta-heuristic algorithms. - Perform Qualitative and Quantitative Analysis of algorithms. @@ -108,7 +108,7 @@ Our goals are to implement all of the classical as well as the state-of-the-art * Install the stable (latest) version from [PyPI release](https://pypi.python.org/pypi/mealpy): ```sh -$ pip install mealpy==3.0.0 +$ pip install mealpy==3.0.1 ``` * Install the alpha/beta version from PyPi @@ -152,24 +152,27 @@ Based on the table below, you can select an appropriate type of decision variabl
-| Class | Syntax | Problem Types | -|----------------|------------------------------------------------------|----------------------------| -| FloatVar | `FloatVar(lb=(-10., )*7, ub=(10., )*7, name="delta")` | Continuous Problem | -| IntegerVar | `IntegerVar(lb=(-10., )*7, ub=(10., )*7, name="delta")` | LP, IP, NLP, QP, MIP | -| StringVar | `StringVar(valid_sets=(("auto", "backward", "forward"), ("leaf", "branch", "root")), name="delta")
` | ML, AI-optimize | -| BinaryVar | `BinaryVar(n_vars=11, name="delta")` | Networks | -| BoolVar | `BoolVar(n_vars=11, name="delta")` | ML, AI-optimize | -| PermutationVar | `PermutationVar(valid_set=(-10, -4, 10, 6, -2), name="delta")` | Combinatorial Optimization | -| MixedSetVar | `MixedSetVar(valid_sets=(("auto", 2, 3, "backward", True), (0, "tournament", "round-robin")), name="delta")` | MIP, MILP | +| Class | Syntax | Problem Types | +|-----------------|--------------------------------------------------------------------------------------------------------------|-----------------------------| +| FloatVar | `FloatVar(lb=(-10., )*7, ub=(10., )*7, name="delta")` | Continuous Problem | +| IntegerVar | `IntegerVar(lb=(-10., )*7, ub=(10., )*7, name="delta")` | LP, IP, NLP, QP, MIP | +| StringVar | `StringVar(valid_sets=(("auto", "backward", "forward"), ("leaf", "branch", "root")), name="delta")` | ML, AI-optimize | +| BinaryVar | `BinaryVar(n_vars=11, name="delta")` | Networks | +| BoolVar | `BoolVar(n_vars=11, name="delta")` | ML, AI-optimize | +| PermutationVar | `PermutationVar(valid_set=(-10, -4, 10, 6, -2), name="delta")` | Combinatorial Optimization | +| MixedSetVar | `MixedSetVar(valid_sets=(("auto", 2, 3, "backward", True), (0, "tournament", "round-robin")), name="delta")` | MIP, MILP | +| TransferBoolVar | `TransferBoolVar(n_vars=11, name="delta", tf_func="sstf_02")` | ML, AI-optimize, Feature | +|TransferBinaryVar| `TransferBinaryVar(n_vars=11, name="delta", tf_func="vstf_04")` | Networks, Feature Selection |
- Let's go through a basic and advanced example. ### Simple Benchmark Function +**Using Problem dict** + ```python from mealpy import FloatVar, SMA import numpy as np @@ -190,6 +193,94 @@ g_best = model.solve(problem) print(f"Best solution: {g_best.solution}, Best fitness: {g_best.target.fitness}") ``` +**Define a custom Problem class** + +```python +from mealpy import Problem, FloatVar, BBO +import numpy as np + +# Our custom problem class +class Squared(Problem): + def __init__(self, bounds=None, minmax="min", name="Squared", data=None, **kwargs): + self.name = name + self.data = data + super().__init__(bounds, minmax, **kwargs) + + def obj_func(self, solution): + x = self.decode_solution(solution)["my_var"] + return np.sum(x ** 2) + +## Now, we define an algorithm, and pass an instance of our *Squared* class as the problem argument. +bound = FloatVar(lb=(-10., )*20, ub=(10., )*20, name="my_var") +problem = Squared(bounds=bound, minmax="min", name="Squared", data="Amazing") +model = BBO.OriginalBBO(epoch=100, pop_size=20) +g_best = model.solve(problem) +``` + +#### The benefit of using custom Problem class + +**Please note that, there is no more `generate_position`, `amend_solution`, and `fitness_function` in Problem class.** +We take care everything under the DataType Class above. Just choose which one fit for your problem. +**We recommend you define a custom class that inherit `Problem` class if your decision variable is not FloatVar** + +```python +from sklearn.svm import SVC +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn import datasets, metrics + +from mealpy import FloatVar, StringVar, IntegerVar, BoolVar, MixedSetVar, SMA, Problem + + +# Load the data set; In this example, the breast cancer dataset is loaded. +X, y = datasets.load_breast_cancer(return_X_y=True) + +# Create training and test split +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y) + +sc = StandardScaler() +X_train_std = sc.fit_transform(X_train) +X_test_std = sc.transform(X_test) + +data = [X_train_std, X_test_std, y_train, y_test] + + +class SvmOptimizedProblem(Problem): + def __init__(self, bounds=None, minmax="max", data=None, **kwargs): + self.data = data + super().__init__(bounds, minmax, **kwargs) + + def obj_func(self, x): + x_decoded = self.decode_solution(x) + C_paras, kernel_paras = x_decoded["C_paras"], x_decoded["kernel_paras"] + degree, gamma, probability = x_decoded["degree_pras"], x_decoded["gamma_paras"], x_decoded["probability_paras"] + + svc = SVC(C=C_paras, kernel=kernel_paras, degree=degree, + gamma=gamma, probability=probability, random_state=1) + # Fit the model + svc.fit(X_train_std, y_train) + # Make the predictions + y_predict = svc.predict(X_test_std) + # Measure the performance + return metrics.accuracy_score(y_test, y_predict) + +my_bounds = [ + FloatVar(lb=0.01, ub=1000., name="C_paras"), + StringVar(valid_sets=('linear', 'poly', 'rbf', 'sigmoid'), name="kernel_paras"), + IntegerVar(lb=1, ub=5, name="degree_paras"), + MixedSetVar(valid_sets=('scale', 'auto', 0.01, 0.05, 0.1, 0.5, 1.0), name="gamma_paras"), + BoolVar(n_vars=1, name="probability_paras"), +] +problem = SvmOptimizedProblem(bounds=my_bounds, minmax="max", data=data) +model = SMA.OriginalSMA(epoch=100, pop_size=20) +model.solve(problem) + +print(f"Best agent: {model.g_best}") +print(f"Best solution: {model.g_best.solution}") +print(f"Best accuracy: {model.g_best.target.fitness}") +print(f"Best parameters: {model.problem.decode_solution(model.g_best.solution)}") +``` + ### Set Seed for Optimizer (So many people asking for this feature) @@ -197,7 +288,7 @@ You can set random seed number for each run of single optimizer. ```python model = SMA.OriginalSMA(epoch=100, pop_size=50, pr=0.03) -g_best = model.solve(problem=problem, seed=10) # Default seed=None +g_best = model.solve(problem=problem, seed=10) # Default seed=None ``` diff --git a/run.py b/run.py index cfc9669c..ab35c665 100644 --- a/run.py +++ b/run.py @@ -15,7 +15,8 @@ from mealpy import TDO, STO, SSpiderO, SSpiderA, SSO, SSA, SRSR, SLO, SHO, SFO, ServalOA, SeaHO, SCSO, POA from mealpy import PFA, OOA, NGO, NMRA, MSA, MRFO, MPA, MGO, MFO, JA, HHO, HGS, HBA, GWO, GTO, GOA from mealpy import GJO, FOX, FOA, FFO, FFA, FA, ESOA, EHO, DO, DMOA, CSO, CSA, CoatiOA, COA, BSA -from mealpy import StringVar, FloatVar, BoolVar, PermutationVar, MixedSetVar, IntegerVar, BinaryVar +from mealpy import (StringVar, FloatVar, BoolVar, PermutationVar, MixedSetVar, IntegerVar, BinaryVar, + TransferBinaryVar, TransferBoolVar) from mealpy import Tuner, Multitask, Problem, Optimizer, Termination, ParameterGrid from mealpy import get_all_optimizers, get_optimizer_by_name @@ -52,11 +53,14 @@ def __init__(self, bounds, minmax, name="Squared", **kwargs): super().__init__(bounds, minmax, **kwargs) def obj_func(self, solution): - return np.sum(solution ** 2) + x = self.decode_solution(solution)["variable"] + return np.sum(x ** 2) + bounds = FloatVar(lb=(-15., )*100, ub=(20., )*100, name="variable") P1 = SquaredProblem(bounds=bounds, minmax="min") + if __name__ == "__main__": model = BBO.OriginalBBO(epoch=10, pop_size=30) model = PSO.OriginalPSO(epoch=100, pop_size=50)