python中实现PDP的代码
时间: 2024-03-07 09:48:43 浏览: 20
以下是一个使用Python和scikit-learn库生成部分依赖图的示例代码:
```python
# 导入必要的库
from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.inspection import plot_partial_dependence
import matplotlib.pyplot as plt
# 加载数据集
boston = load_boston()
X, y = boston.data, boston.target
# 训练随机森林回归模型
rf = RandomForestRegressor(n_estimators=100, random_state=42)
rf.fit(X, y)
# 生成部分依赖图
fig, ax = plt.subplots(figsize=(12, 4))
plot_partial_dependence(rf, X, features=[5, 12], ax=ax)
ax.set_title('Partial Dependence Plot')
plt.show()
```
在这个示例代码中,我们使用波士顿房价数据集训练了一个随机森林回归模型。然后,我们使用plot_partial_dependence函数生成了第5个和第12个自变量的部分依赖图。最后,我们使用matplotlib库将图形显示出来。
相关问题
请给我一段变领域搜索算法解决PDP问题的python代码
好的,以下是一段使用变领域搜索算法解决PDP问题的Python代码,其中使用了基于禁忌搜索的变领域搜索算法(Tabu Search):
```python
import numpy as np
class PDP:
def __init__(self, capacity, demand, distance, num_vehicles):
self.capacity = capacity
self.demand = demand
self.distance = distance
self.num_vehicles = num_vehicles
self.num_nodes = len(demand)
self.best_sol = None
def generate_initial_solution(self):
# 随机生成初始解
sol = np.zeros((self.num_nodes, self.num_vehicles))
for i in range(self.num_nodes):
j = np.random.randint(self.num_vehicles)
sol[i, j] = 1
return sol
def calc_obj(self, sol):
# 计算解的目标函数值
obj = 0
for j in range(self.num_vehicles):
load = 0
dist = 0
for i in range(self.num_nodes):
if sol[i, j] == 1:
load += self.demand[i]
dist += self.distance[i][0]
elif sol[i, j] == 2:
load -= self.demand[i]
dist += self.distance[0][i]
if load > self.capacity:
return np.inf
dist += self.distance[0][j+1]
obj += dist
return obj
def shaking_one(self, sol):
# 变邻域操作1
route_list = []
for j in range(self.num_vehicles):
idx = np.where(sol[:, j] == 1)[0]
if len(idx) > 0:
i = np.random.choice(idx)
k = np.random.randint(self.num_vehicles)
while k == j:
k = np.random.randint(self.num_vehicles)
sol[i, j] = 0
sol[i, k] = 1
route_list.append(((i, j), (i, k)))
return route_list
def shaking_two(self, sol):
# 变邻域操作2
route_list = []
for j in range(self.num_vehicles):
for i1 in range(self.num_nodes):
if sol[i1, j] == 1:
for i2 in range(self.num_nodes):
if sol[i2, j] == 0 and i1 != i2:
sol[i1, j] = 0
sol[i2, j] = 1
if self.calc_obj(sol) < np.inf:
route_list.append(((i1, j), (i2, j)))
return route_list
sol[i1, j] = 1
sol[i2, j] = 0
return route_list
def tabu_search(self, max_iter, tabu_len):
# 变领域搜索算法(基于禁忌搜索)
sol = self.generate_initial_solution()
best_sol = sol.copy()
tabu_list = []
tabu_iter = np.zeros((self.num_nodes, self.num_vehicles))
iter_cnt = 0
while iter_cnt < max_iter:
iter_cnt += 1
route_list1 = self.shaking_one(sol)
route_list2 = self.shaking_two(sol)
if len(route_list1) > 0 or len(route_list2) > 0:
best_obj = np.inf
best_route = None
for route in route_list1 + route_list2:
i1, j1 = route[0]
i2, j2 = route[1]
if tabu_iter[i1, j1] > iter_cnt or tabu_iter[i2, j2] > iter_cnt:
continue
sol[i1, j1] = 0
sol[i2, j2] = 1
obj = self.calc_obj(sol)
if obj < best_obj:
best_obj = obj
best_sol = sol.copy()
best_route = route
if best_route is not None:
i1, j1 = best_route[0]
i2, j2 = best_route[1]
sol[i1, j1] = 0
sol[i2, j2] = 1
tabu_list.append(best_route)
tabu_iter[i1, j1] = iter_cnt + tabu_len
tabu_iter[i2, j2] = iter_cnt + tabu_len
else:
break
self.best_sol = best_sol
return best_sol
```
上述代码中,PDP类封装了PDP问题的求解过程,包括随机生成初始解、计算目标函数值、变领域操作等。tabu_search方法实现了基于禁忌搜索的变领域搜索算法,其中max_iter参数表示最大迭代次数,tabu_len参数表示禁忌长度。在每一次迭代中,算法先进行变领域操作,然后从所有可行的邻域解中选择一个最优解,并更新当前解。如果找不到可行的邻域解,则算法终止。在搜索过程中,算法使用禁忌列表保存已经搜索过的解,以避免陷入局部最优解。
我想看两个特征相互作用下的PDP的三维图,应该用什么方法实现?
您可以使用Python中的Plotly库来实现三维PDP图。以下是一个示例代码:
```python
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.inspection import plot_partial_dependence
# 加载数据集
boston = load_boston()
# 训练随机森林回归模型
rf = RandomForestRegressor(n_estimators=100, random_state=42)
rf.fit(boston.data, boston.target)
# 绘制PDP
fig = make_subplots(rows=1, cols=1, specs=[[{'type': 'surface'}]])
pdp, axes = plot_partial_dependence(rf, boston.data, [5, 12], n_jobs=-1, grid_resolution=50)
x_axis = pdp[0][0]
y_axis = pdp[0][1]
z_axis = pdp[1].T
fig.add_trace(go.Surface(x=x_axis, y=y_axis, z=z_axis), row=1, col=1)
fig.update_layout(title='PDP 3D Plot', autosize=False, width=800, height=800)
fig.show()
```
在上面的代码中,我们首先使用sklearn的plot_partial_dependence方法来计算PDP,并使用Plotly的Surface图表类型来绘制三维图。请注意,我们使用make_subplots来创建一个包含一个Surface子图的Figure对象。最后,我们使用update_layout方法设置图表的标题、大小等属性,并使用show方法来显示图表。
在这个示例中,我们选择了两个特征(第5个和第12个)来绘制PDP,您可以根据自己的需要选择不同的特征。