柯西与高斯的针对性研究.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[NegativeProxyObjective(\n",
       "   (bf): Sphere()\n",
       " ),\n",
       " NegativeProxyObjective(\n",
       "   (bf): Rosenbrock()\n",
       " ),\n",
       " NegativeProxyObjective(\n",
       "   (bf): Step()\n",
       " ),\n",
       " NegativeProxyObjective(\n",
       "   (bf): NoisyQuartic()\n",
       " ),\n",
       " NegativeProxyObjective(\n",
       "   (bf): Schwefel226()\n",
       " ),\n",
       " NegativeProxyObjective(\n",
       "   (bf): Ackley()\n",
       " ),\n",
       " NegativeProxyObjective(\n",
       "   (bf): Schwefel222()\n",
       " ),\n",
       " NegativeProxyObjective(\n",
       "   (bf): Holder()\n",
       " ),\n",
       " NegativeProxyObjective(\n",
       "   (bf): Camel3()\n",
       " ),\n",
       " NegativeProxyObjective(\n",
       "   (bf): Michal()\n",
       " )]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from typing import List\n",
    "import integer_funs\n",
    "import torch\n",
    "import evolutionary_operator as eo\n",
    "import evolutionary_algorithm as ea\n",
    "from evolutionary_algorithm import mutation_based_ea\n",
    "from evolutionary_algorithm.student_experiment import StudentExperiment\n",
    "import benchmark_funs as bf\n",
    "dataset = list(map(bf.NegativeProxyObjective, bf.benchmark_functions)\n",
    ")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "# mutation_ea = ea.mutation_based_ea.MutationBasedEA(problem)\n",
    "# normal_choice = torch.Tensor([0, 1, -1, -1, 1]).to(torch.int64)\n",
    "normal_choice = torch.Tensor([10, 3, 5, 5, 1]).to(torch.int64)\n",
    "cauthy_choice = torch.Tensor([0, 2, -1, -1, 1]).to(torch.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "def evaluate(dataset: List[bf.BenchmarkFunction], choice: torch.Tensor, experiment_times=30) -> torch.Tensor:\n",
    "    result = torch.zeros(len(dataset), experiment_times)\n",
    "    for i, b_fun in tqdm(enumerate(dataset), '使用数据集评估一个演化算法中'):\n",
    "        mutation_ea = mutation_based_ea.MutationBasedEA(b_fun) \n",
    "        for j in range(experiment_times):\n",
    "            result[i, j] = mutation_ea(choice)[1][-1]-b_fun.optival\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "使用数据集评估一个演化算法中: 10it [00:18,  1.86s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor([[-2.9395e+02, -5.1338e+02, -3.8053e+02, -4.8738e+02, -2.2231e+02,\n",
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       "          -7.0971e+01, -6.0284e+01, -8.2024e+01, -8.2523e+01, -8.6735e+01,\n",
       "          -4.4764e+01, -9.6264e+01, -1.0418e+02, -6.5613e+01, -1.2617e+02,\n",
       "          -8.7756e+01, -9.7277e+01, -1.2714e+02, -8.1635e+01, -8.1973e+01],\n",
       "         [-4.7341e+03, -4.7267e+03, -4.3896e+03, -5.0960e+03, -4.6303e+03,\n",
       "          -4.0884e+03, -4.4637e+03, -4.3173e+03, -4.7323e+03, -4.5783e+03,\n",
       "          -4.5528e+03, -4.8596e+03, -4.6230e+03, -4.0793e+03, -4.2868e+03,\n",
       "          -5.1024e+03, -4.3606e+03, -4.4381e+03, -4.7225e+03, -5.0368e+03,\n",
       "          -4.6883e+03, -4.0200e+03, -5.1573e+03, -5.1687e+03, -4.7356e+03,\n",
       "          -4.6487e+03, -4.2746e+03, -4.5285e+03, -4.7493e+03, -4.7743e+03],\n",
       "         [-1.9960e+01, -1.8043e+01, -1.7878e+01, -1.6721e+01, -1.6116e+01,\n",
       "          -1.9047e+01, -1.9277e+01, -1.7742e+01, -1.9961e+01, -1.9961e+01,\n",
       "          -1.7740e+01, -1.8862e+01, -1.7826e+01, -1.9963e+01, -1.7393e+01,\n",
       "          -1.6322e+01, -1.7492e+01, -1.4948e+01, -1.8375e+01, -1.9939e+01,\n",
       "          -1.6001e+01, -1.9961e+01, -1.7768e+01, -1.7029e+01, -1.9963e+01,\n",
       "          -1.9685e+01, -1.6153e+01, -1.9868e+01, -1.7612e+01, -1.6621e+01],\n",
       "         [-5.5055e+02, -1.2707e+03, -5.2453e+02, -1.1362e+03, -7.2610e+02,\n",
       "          -4.5074e+04, -5.2045e+02, -8.1783e+02, -5.2899e+02, -2.8888e+02,\n",
       "          -7.1057e+02, -6.8913e+02, -2.8435e+06, -6.7881e+02, -4.7648e+02,\n",
       "          -3.2527e+02, -6.9849e+02, -7.8953e+02, -1.9282e+02, -8.5801e+02,\n",
       "          -1.1394e+06, -6.2423e+02, -7.6957e+02, -1.0904e+03, -1.2734e+03,\n",
       "          -5.1929e+02, -1.1623e+05, -9.3929e+02, -3.5010e+02, -6.5001e+02],\n",
       "         [ 5.6305e-06,  5.6305e-06, -9.1553e-08, -7.7209e-06,  3.7231e-06,\n",
       "           7.5378e-06,  7.5378e-06,  3.7231e-06, -9.6283e-06, -1.3443e-05,\n",
       "           3.7231e-06,  5.6305e-06,  5.6305e-06, -3.5096e-03, -2.2897e-04,\n",
       "          -1.5350e-05,  3.7231e-06,  7.5378e-06,  5.6305e-06, -3.1892e-03,\n",
       "          -1.7258e-05, -1.1263e-04,  7.5378e-06,  3.7231e-06,  7.5378e-06,\n",
       "          -6.3034e-05,  7.5378e-06,  5.6305e-06,  9.4452e-06, -4.8074e-04],\n",
       "         [-3.4716e-12, -4.7975e-06, -4.8711e-09, -8.4182e-08, -2.2560e-09,\n",
       "          -7.0532e-12, -8.8403e-06, -1.4384e-07, -7.6769e-05, -9.6047e-04,\n",
       "          -8.6845e-05, -1.2525e-02, -2.2095e-06, -2.1795e-09, -5.6045e-07,\n",
       "          -6.5430e-06, -1.2857e-07, -5.4455e-08, -3.7959e-07, -1.1156e-09,\n",
       "          -6.8260e-09, -8.1523e-03, -1.6538e-03, -4.2338e-05, -6.6523e-09,\n",
       "          -9.5807e-07, -3.0143e-07, -5.8992e-06, -1.0592e-08, -4.8049e-07],\n",
       "         [-2.1051e-05, -1.3817e-06, -3.1323e-04, -5.9091e-02,  3.8635e-06,\n",
       "          -4.0005e-05, -1.3660e-05, -1.4614e-05, -1.6879e-05,  2.6714e-06,\n",
       "           2.9099e-06,  3.1483e-06,  3.8635e-06,  2.9099e-06, -4.2801e-07,\n",
       "          -1.7393e-06,  2.9099e-06,  4.8828e-08, -2.3050e-03, -4.8101e-01,\n",
       "          -2.6289e-03, -6.6643e-07, -7.4020e-03, -5.8483e-05, -6.5397e-05,\n",
       "          -1.7393e-06, -1.6201e-06,  3.1483e-06,  1.0025e-06,  3.8635e-06]]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_result = evaluate(dataset, normal_choice)\n",
    "cauthy_result = evaluate(dataset, cauthy_choice)\n",
    "normal_result, cauthy_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/ssc-07/team/package_managers/anaconda3/envs/torch2.0/lib/python3.9/site-packages/scipy/stats/_morestats.py:3255: UserWarning: Exact p-value calculation does not work if there are zeros. Switching to normal approximation.\n",
      "  warnings.warn(\"Exact p-value calculation does not work if there are \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. , 63.5,  0. ,  3. ]),\n",
       " array([1.86264515e-09, 1.86264515e-09, 1.86264515e-09, 1.86264515e-09,\n",
       "        1.86264515e-09, 1.86264515e-09, 1.86264515e-09, 8.55558143e-04,\n",
       "        1.86264515e-09, 5.23798844e-06]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_mean = torch.mean(normal_result, dim=1)\n",
    "cauthy_mean = torch.mean(cauthy_result, dim=1)\n",
    "from scipy.stats import wilcoxon\n",
    "stat, p = wilcoxon(normal_result.T, cauthy_result.T, alternative='two-sided')\n",
    "stat, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({'最终算法':normal_mean, 'cauthy':cauthy_mean, 'ranksum':stat, \"p-value\":p})\n",
    "df.head()\n",
    "df.to_excel(\"柯西与高斯对比.xlsx\")"
   ]
  }
 ],
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