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thieupu committed Nov 5, 2023
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121 changes: 106 additions & 15 deletions README.md
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from openpyxl.descriptors import Integer
<p align="center">
<img style="height:400px;"
src="https://thieu1995.github.io/post/2022-04/19-mealpy-tutorials/mealpy5-nobg.png"
Expand All @@ -8,7 +8,7 @@ alt="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)
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<details><summary><h2>Goals</h2></summary>

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.
Expand All @@ -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
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<div align="center">

| 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")<br/>` | 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 |

</div>


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
Expand All @@ -190,14 +193,102 @@ 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)

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
```


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8 changes: 6 additions & 2 deletions run.py
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Expand Up @@ -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

Expand Down Expand Up @@ -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)
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