AlModelTrain.py

# -*- coding: utf-8 -*-
import networkx as nx
import matplotlib.pyplot as plt
import NetworkModel
import RandomGraph
import LatencyCount
import MigrationPathStatus
from scipy.optimize import linear_sum_assignment
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import pickle
import NetworkModel


def AI_model_learning(G, aim, bvnf_sorted, bvnf_mig_loc):
    """
    利用Kuhn_Munkres_improvement和RandomAlgorithm生成数据进行训练并保存模型
    :param G:
    :param aim:
    :param bvnf_sorted:
    :param bvnf_mig_loc:
    :return:
    """

    x_train = []
    y_train = []

    # 此处的训练集应该更大，到时候改
    s1 = MigrationPathStatus.Kuhn_Munkres_improvement(G, aim, bvnf_sorted, bvnf_mig_loc)
    for i in s1:
        x_train.append([i.MigTime_pre, i.MigTime_after, i.priority])
        y_train.append(1)
    s0 = MigrationPathStatus.RandomAlgorithm(G, aim, bvnf_sorted, bvnf_mig_loc)
    for i in s0:
        x_train.append([i.MigTime_pre, i.MigTime_after, i.priority])
        y_train.append(0)

    # 构建随机森林模型，训练并预测
    clf = RandomForestClassifier(n_estimators=10)
    clf.fit(x_train, y_train)

    # 假设模型对象为clf
    with open('model.pkl', 'wb') as f:
        pickle.dump(clf, f)

    print("保存完成")


if __name__ == '__main__':

    # for i in ``` 利用循环随机生成足够的训练数据（暂时只写一组，没写循环）

    G = RandomGraph.NewRandomGraph(70)

    # 将图中任意两点之间的最小值求出
    G = NetworkModel.dijkstra(G)
    # NetworkModel.ShowGraph(G)
    # print(G.get_edge_data("4", "1")['weight'])


    # 实例化一个用户请求r
    r = NetworkModel.UserRequests([0, 1, 2], 2, 1)

    # 已知主VNF部署在vnf_loc，备份部署位置保存在列表 bvnf_loc中
    vnf_loc = r.F[0]
    bvnf_loc = r.F[1:]

    # 已知用户从u移动到v，主VNF即将跟着从u移动到v（FM-MEC）
    # 用户从0到4 （数据生成时此次可以随机生成）
    aim = 4

    # 求迁移后的平均故障时延afl
    afl = LatencyCount.get_average_failure_latency(G, r, aim)
    print("迁移后的afl:", afl)

    # 如果迁移后的平均故障时延afl比用户所能容忍的高，则需要迁移
    if afl > r.T_err:
        print("需要进行迁移，用数据进行模型训练")
        # 确定出状态$s_i=(u_i,v_i)$
        # s = MigrationPathStatus.migration_status(G, r, aim)

        # vnf_loc = r.F[0]
        # 将bvnf按离主实例迁移后位置的权值从小到大排序
        bvnf_sorted = MigrationPathStatus.bvnf_sort(G, aim, r.F[1:])

        # 获取离aim最远的bvnf的边的权值
        max_range = G.get_edge_data(aim, bvnf_sorted[-1])["weight"]
        # print("max_range:", max_range)

        # 选择出到点aim的边的权值小于max_range的点（不包括已经部署了bvnf的点）
        adjacent_nodes = MigrationPathStatus.get_adjacent_points(G, aim, max_range)
        # print("离目标点距离小于max的点:", adjacent_nodes)
        not_migration_loc = r.F[1:]
        not_migration_loc.append(aim)
        bvnf_mig_loc = [x for x in adjacent_nodes if x not in not_migration_loc]  # bvnf可以迁移的位置

        AI_model_learning(G, aim, bvnf_sorted, bvnf_mig_loc)