cost_maker.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('results/results_100_10000.csv', header=None)\n",
    "results = df.to_numpy()\n",
    "X=results[:100,:-1]\n",
    "y=(results[:100,-1]-0.5)*10 # this is currently linear\n",
    "# may want to make y exponential or logarithmic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "x_unique = np.unique(X)\n",
    "num_units = (len(x_unique)-1)//2\n",
    "costs = np.zeros(num_units+1)\n",
    "print(costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "(keys, values) = np.unique(X[0], return_counts=True)\n",
    "neg_index = np.nonzero(keys<0)\n",
    "neg_cost = np.sum(costs[np.abs(keys[neg_index]).astype(int)]*values[neg_index])\n",
    "pos_index = np.nonzero(keys>0)\n",
    "pos_cost = np.sum(costs[np.abs(keys[pos_index]).astype(int)]*values[pos_index])\n",
    "delta_cost = pos_cost-neg_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MSE(X, y, costs):\n",
    "    total_error = 0\n",
    "    for row in range(X.shape[0]):\n",
    "        (keys, values) = np.unique(X[row], return_counts=True)\n",
    "        neg_index = np.nonzero(keys<0)\n",
    "        neg_cost = np.sum(costs[np.abs(keys[neg_index]).astype(int)]*values[neg_index])\n",
    "        pos_index = np.nonzero(keys>0)\n",
    "        pos_cost = np.sum(costs[np.abs(keys[pos_index]).astype(int)]*values[pos_index])\n",
    "        delta_cost = pos_cost-neg_cost\n",
    "        total_error += (y[row]-delta_cost)**2\n",
    "        # if row%1000 == 0:\n",
    "        #     print(row)\n",
    "    return total_error/X.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.010399999999997\n"
     ]
    }
   ],
   "source": [
    "print(MSE(X, y, costs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def perturb(costs, rate=1, min_change=0.1):\n",
    "#     return np.maximum(0,costs+((np.random.randn(len(costs))-0.2)*rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "137 0 2419.8938866085823\n",
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      "138 0 913.4091129288576\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "139 0 1441.842300921901\n",
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      "140 0 891.8066202594428\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "141 0 3070.2159242756384\n",
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      "142 0 55.10491331520033\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "143 0 785.1737583307441\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "144 0 1083.4932333815727\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "145 0 4397.261181356526\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "146 0 3598.266252884334\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "147 0 1932.6706821984983\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "148 0 495.3704100092464\n",
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      "149 0 2728.0998987332623\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "150 0 4102.144867994052\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "151 0 3194.7228872121605\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "152 0 183.45195633158164\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "153 0 794.0409522575508\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "154 0 2948.835019675306\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "155 0 6360.261007744528\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "156 0 431.3880200982028\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "157 0 126.92484352234236\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "158 0 1226.0580155214498\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "159 0 1180.3431392721802\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "160 0 42.814242479566694\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "161 0 1930.871030509853\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "162 0 49.441150717916464\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "163 0 12.010399999999997\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "164 0 2311.475757141324\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "165 0 1879.400893283055\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "166 0 39.45259665913306\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "167 0 12.653886248769009\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "168 0 628.7784700027465\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "169 0 3361.715863386338\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "170 0 106.91468677564234\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "171 0 4668.488351834115\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "172 0 21.368770038139733\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "173 0 12.010399999999997\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "174 0 157.29246745051552\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "175 0 523.4839002750505\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "176 0 266.5275370152285\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "177 0 1545.8689692028313\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "178 0 1779.539821331921\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "179 0 1123.8635398951783\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "180 0 338.54790269420204\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "181 0 360.78698718235006\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "182 0 3269.504294548936\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "183 0 45.72179992228346\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "184 0 927.7021211167106\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "185 0 1863.596750390537\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "186 0 2402.0563674723408\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "187 0 12.010399999999997\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "188 0 1706.3116278953908\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "189 0 38.5211360877485\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "190 0 1402.6432889539976\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "191 0 2102.996277535694\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "192 0 12.010399999999997\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "193 0 6536.795498026047\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "194 0 474.7497595130636\n",
      "[1.95525464 0.         0.         0.         0.        ] 12.010399999999997\n",
      "195 0 2427.0807809486478\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\co41c\\OneDrive - ucsc.edu\\UCSC\\Sophmore\\CSE 144\\ai-simulation-agent\\cost_maker.ipynb Cell 8\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/co41c/OneDrive%20-%20ucsc.edu/UCSC/Sophmore/CSE%20144/ai-simulation-agent/cost_maker.ipynb#X10sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(epochs):\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/co41c/OneDrive%20-%20ucsc.edu/UCSC/Sophmore/CSE%20144/ai-simulation-agent/cost_maker.ipynb#X10sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m     new_costs \u001b[39m=\u001b[39m perturb(costs,rate\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m\u001b[39m-\u001b[39mepoch\u001b[39m/\u001b[39mepochs)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/co41c/OneDrive%20-%20ucsc.edu/UCSC/Sophmore/CSE%20144/ai-simulation-agent/cost_maker.ipynb#X10sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m     new_error \u001b[39m=\u001b[39m MSE(X,y,new_costs)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/co41c/OneDrive%20-%20ucsc.edu/UCSC/Sophmore/CSE%20144/ai-simulation-agent/cost_maker.ipynb#X10sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m     \u001b[39mif\u001b[39;00m new_error \u001b[39m<\u001b[39m old_error:\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/co41c/OneDrive%20-%20ucsc.edu/UCSC/Sophmore/CSE%20144/ai-simulation-agent/cost_maker.ipynb#X10sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m         old_error \u001b[39m=\u001b[39m new_error\n",
      "\u001b[1;32mc:\\Users\\co41c\\OneDrive - ucsc.edu\\UCSC\\Sophmore\\CSE 144\\ai-simulation-agent\\cost_maker.ipynb Cell 8\u001b[0m in \u001b[0;36mMSE\u001b[1;34m(X, y, costs)\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/co41c/OneDrive%20-%20ucsc.edu/UCSC/Sophmore/CSE%20144/ai-simulation-agent/cost_maker.ipynb#X10sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m (keys, values) \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39munique(X[row], return_counts\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/co41c/OneDrive%20-%20ucsc.edu/UCSC/Sophmore/CSE%20144/ai-simulation-agent/cost_maker.ipynb#X10sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m neg_index \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mnonzero(keys\u001b[39m<\u001b[39m\u001b[39m0\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/co41c/OneDrive%20-%20ucsc.edu/UCSC/Sophmore/CSE%20144/ai-simulation-agent/cost_maker.ipynb#X10sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m neg_cost \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msum(costs[np\u001b[39m.\u001b[39;49mabs(keys[neg_index])\u001b[39m.\u001b[39;49mastype(\u001b[39mint\u001b[39;49m)]\u001b[39m*\u001b[39;49mvalues[neg_index])\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/co41c/OneDrive%20-%20ucsc.edu/UCSC/Sophmore/CSE%20144/ai-simulation-agent/cost_maker.ipynb#X10sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m pos_index \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mnonzero(keys\u001b[39m>\u001b[39m\u001b[39m0\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/co41c/OneDrive%20-%20ucsc.edu/UCSC/Sophmore/CSE%20144/ai-simulation-agent/cost_maker.ipynb#X10sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m pos_cost \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msum(costs[np\u001b[39m.\u001b[39mabs(keys[pos_index])\u001b[39m.\u001b[39mastype(\u001b[39mint\u001b[39m)]\u001b[39m*\u001b[39mvalues[pos_index])\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# costs = np.zeros((len(x_unique)-1)//2+1)\n",
    "# min_error = 99999\n",
    "# min_costs = costs\n",
    "# epochs = 10\n",
    "# for generation in range(10000):\n",
    "#     costs = np.random.randn(len(min_costs))*10\n",
    "#     old_error = MSE(X,y,costs)\n",
    "#     for epoch in range(epochs):\n",
    "#         new_costs = perturb(costs,rate=1-epoch/epochs)\n",
    "#         new_error = MSE(X,y,new_costs)\n",
    "#         if new_error < old_error:\n",
    "#             old_error = new_error\n",
    "#             costs = new_costs\n",
    "#             # print(generation, epoch, old_error)\n",
    "#         if epoch%10==0:\n",
    "#             print(generation, epoch, old_error)\n",
    "#     if old_error < min_error:\n",
    "#         min_error = old_error\n",
    "#         min_costs = np.copy(costs)\n",
    "#     print(min_costs, min_error)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([[0, 1, 1, 2, 2],\n",
      "       [1, 0, 1, 1, 2]]), array([1, 1, 1, 1, 1], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "test = np.array([[1,2,1,2,0],[1,2,0,1,1]])\n",
    "print(np.unique(test, return_counts=True, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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