假设在excel中有4列自变量1列因变量,用结合遗传算法的符号回归生成能表达这5个变量的数学公式的代码
时间: 2024-09-23 18:06:05 浏览: 28
在Excel中使用结合遗传算法的符号回归生成公式通常涉及到编程步骤,这里我们将提供一种基于Python的简单示例,使用`pandas`处理数据,`deap`库进行遗传算法,以及`sklearn`库用于符号函数。请注意,这个例子假设你已经安装了相关的库。
```python
import pandas as pd
from sklearn import linear_model
from sympy import symbols, lambdify, Eq
from deap import base, creator, tools
# 假设df是你的DataFrame,columns包含自变量名,target是因变量
df = pd.read_excel('your_file.xlsx')
columns = df.columns[:-1] # 去掉最后一列
target = 'your_target_column'
# 定义符号函数
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
def eval_regression(individual):
formula_str = "".join(str(i) for i in individual)
try:
model = Eq(target, lambdify(tuple(columns), formula_str))
regression = linear_model.LinearRegression()
regression.fit(df[columns].values, df[target].values)
# 计算模型的R^2或其他评估指标
score = regression.score(df[columns], df[target])
return (score,), True
except Exception as e:
print(f"Error in formula: {formula_str}, error message: {str(e)}")
return (0.0,), False
toolbox = base.Toolbox()
toolbox.register("individual", tools.initRepeat, creator.Individual, creator.Symbol('x', columns))
# 遗传算法设置
toolbox.register("evaluate", eval_regression)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)
# 初始化种群,运行GA
population_size = 100
pop = toolbox.population(n=population_size)
hof = tools.HallOfFame(1)
for gen in range(100): # 可以根据需要调整迭代次数
offspring = toolbox.select(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
# 交叉和变异
offspring = toolbox.mate(offspring, randomize=True)
offspring = [toolbox.mutate(ind) for ind in offspring]
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
pop[:] = offspring
best_formula = hof[0]
print(f"Best formula: {best_formula}")
```