currentmatches.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import warnings\n",
    "import sklearn\n",
    "import sklearn.model_selection\n",
    "np.set_printoptions(precision=3, suppress=True)  # Print as 0.001 instead of 9.876e-4\n",
    "torch.set_printoptions(precision=3, sci_mode=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding match data from file\n",
    "\n",
    "data = np.loadtxt('newmatchdata.csv',skiprows=1,delimiter=',')\n",
    "X_data = np.array(data[:,1:]).astype(np.int32)\n",
    "y_data = np.array(data[:,:1]).astype(np.int32)\n",
    "\n",
    "#turn all data into feature vector\n",
    "#feature vector creation\n",
    "X_val_trn = torch.zeros((40000,138*2),dtype=torch.float32)\n",
    "j = 0\n",
    "for d in X_data:\n",
    "    for i in range(len(d)):\n",
    "        if(i < len(d)/2):\n",
    "            h = d[i]\n",
    "            X_val_trn[j][h] += 1\n",
    "            \n",
    "            \n",
    "        else:\n",
    "            h = d[i]\n",
    "            X_val_trn[j][h+137] += 1\n",
    "      \n",
    "    j += 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\david\\AppData\\Local\\Temp\\ipykernel_16504\\337929334.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  X_train = torch.tensor(X_train,dtype=torch.float32)\n",
      "C:\\Users\\david\\AppData\\Local\\Temp\\ipykernel_16504\\337929334.py:12: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  X_val = torch.tensor(X_val,dtype=torch.float32)\n",
      "C:\\Users\\david\\AppData\\Local\\Temp\\ipykernel_16504\\337929334.py:16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  X_test = torch.tensor(X_test,dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#sklearn train test split\n",
    "\n",
    "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X_val_trn,y_data, test_size = 0.2, shuffle= True)\n",
    "\n",
    "#split train into train/validation set\n",
    "\n",
    "X_train, X_val, y_train, y_val = sklearn.model_selection.train_test_split(X_train,y_train, test_size = 0.15, shuffle= True)\n",
    "\n",
    "X_train = torch.tensor(X_train,dtype=torch.float32)\n",
    "y_train = torch.tensor(y_train,dtype=torch.float32)\n",
    "\n",
    "X_val = torch.tensor(X_val,dtype=torch.float32)\n",
    "\n",
    "y_val = torch.tensor(y_val,dtype=torch.float32)\n",
    "\n",
    "X_test = torch.tensor(X_test,dtype=torch.float32)\n",
    "y_test = torch.tensor(y_test,dtype=torch.float32)\n",
    "#sklearn train test split\n",
    "#use test set to calculate error, CCE, accuracy etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#model creation\n",
    "#addition of multiple hidden layers and drop rate to help with regularization\n",
    "print(torch.cuda.is_available())\n",
    "model = torch.nn.Sequential(\n",
    "    torch.nn.Linear(276,100),\n",
    "    torch.nn.Dropout(),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(100,50),\n",
    "    torch.nn.Dropout(),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(50,1)\n",
    ")\n",
    "\n",
    "model2 = torch.nn.Sequential(\n",
    "    torch.nn.Linear(276,1)\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  1: loss on final training batch: 0.6955\n",
      "Epoch  1: accuracy on validation set: 0.4977\n",
      "Epoch  1: accuracy on training set: 0.4920\n",
      "Epoch  1: loss on validation set: 0.6953\n",
      "Epoch  2: loss on final training batch: 0.6917\n",
      "Epoch  2: accuracy on validation set: 0.4977\n",
      "Epoch  2: accuracy on training set: 0.4920\n",
      "Epoch  2: loss on validation set: 0.6949\n",
      "Epoch  3: loss on final training batch: 0.6905\n",
      "Epoch  3: accuracy on validation set: 0.4977\n",
      "Epoch  3: accuracy on training set: 0.4920\n",
      "Epoch  3: loss on validation set: 0.6946\n",
      "Epoch  4: loss on final training batch: 0.6907\n",
      "Epoch  4: accuracy on validation set: 0.4977\n",
      "Epoch  4: accuracy on training set: 0.4920\n",
      "Epoch  4: loss on validation set: 0.6944\n",
      "Epoch  5: loss on final training batch: 0.6913\n",
      "Epoch  5: accuracy on validation set: 0.4977\n",
      "Epoch  5: accuracy on training set: 0.4920\n",
      "Epoch  5: loss on validation set: 0.6942\n",
      "Epoch  6: loss on final training batch: 0.6896\n",
      "Epoch  6: accuracy on validation set: 0.4977\n",
      "Epoch  6: accuracy on training set: 0.4920\n",
      "Epoch  6: loss on validation set: 0.6941\n",
      "Epoch  7: loss on final training batch: 0.6912\n",
      "Epoch  7: accuracy on validation set: 0.4977\n",
      "Epoch  7: accuracy on training set: 0.4920\n",
      "Epoch  7: loss on validation set: 0.6940\n",
      "Epoch  8: loss on final training batch: 0.6931\n",
      "Epoch  8: accuracy on validation set: 0.4977\n",
      "Epoch  8: accuracy on training set: 0.4920\n",
      "Epoch  8: loss on validation set: 0.6939\n",
      "Epoch  9: loss on final training batch: 0.6921\n",
      "Epoch  9: accuracy on validation set: 0.4977\n",
      "Epoch  9: accuracy on training set: 0.4920\n",
      "Epoch  9: loss on validation set: 0.6938\n",
      "Epoch 10: loss on final training batch: 0.6915\n",
      "Epoch 10: accuracy on validation set: 0.4977\n",
      "Epoch 10: accuracy on training set: 0.4920\n",
      "Epoch 10: loss on validation set: 0.6937\n",
      "Epoch 11: loss on final training batch: 0.6909\n",
      "Epoch 11: accuracy on validation set: 0.4977\n",
      "Epoch 11: accuracy on training set: 0.4920\n",
      "Epoch 11: loss on validation set: 0.6937\n",
      "Epoch 12: loss on final training batch: 0.6910\n",
      "Epoch 12: accuracy on validation set: 0.4977\n",
      "Epoch 12: accuracy on training set: 0.4920\n",
      "Epoch 12: loss on validation set: 0.6936\n",
      "Epoch 13: loss on final training batch: 0.6904\n",
      "Epoch 13: accuracy on validation set: 0.4977\n",
      "Epoch 13: accuracy on training set: 0.4920\n",
      "Epoch 13: loss on validation set: 0.6936\n",
      "Epoch 14: loss on final training batch: 0.6954\n",
      "Epoch 14: accuracy on validation set: 0.4977\n",
      "Epoch 14: accuracy on training set: 0.4920\n",
      "Epoch 14: loss on validation set: 0.6935\n",
      "Epoch 15: loss on final training batch: 0.6940\n",
      "Epoch 15: accuracy on validation set: 0.4977\n",
      "Epoch 15: accuracy on training set: 0.4920\n",
      "Epoch 15: loss on validation set: 0.6935\n",
      "Epoch 16: loss on final training batch: 0.6952\n",
      "Epoch 16: accuracy on validation set: 0.4977\n",
      "Epoch 16: accuracy on training set: 0.4920\n",
      "Epoch 16: loss on validation set: 0.6935\n",
      "Epoch 17: loss on final training batch: 0.6936\n",
      "Epoch 17: accuracy on validation set: 0.4977\n",
      "Epoch 17: accuracy on training set: 0.4920\n",
      "Epoch 17: loss on validation set: 0.6935\n",
      "Epoch 18: loss on final training batch: 0.6950\n",
      "Epoch 18: accuracy on validation set: 0.4977\n",
      "Epoch 18: accuracy on training set: 0.4920\n",
      "Epoch 18: loss on validation set: 0.6934\n",
      "Epoch 19: loss on final training batch: 0.6962\n",
      "Epoch 19: accuracy on validation set: 0.4977\n",
      "Epoch 19: accuracy on training set: 0.4920\n",
      "Epoch 19: loss on validation set: 0.6934\n",
      "Epoch 20: loss on final training batch: 0.6957\n",
      "Epoch 20: accuracy on validation set: 0.4977\n",
      "Epoch 20: accuracy on training set: 0.4920\n",
      "Epoch 20: loss on validation set: 0.6934\n",
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      "Epoch 21: accuracy on validation set: 0.4977\n",
      "Epoch 21: accuracy on training set: 0.4920\n",
      "Epoch 21: loss on validation set: 0.6934\n",
      "Epoch 22: loss on final training batch: 0.6920\n",
      "Epoch 22: accuracy on validation set: 0.4977\n",
      "Epoch 22: accuracy on training set: 0.4920\n",
      "Epoch 22: loss on validation set: 0.6934\n",
      "Epoch 23: loss on final training batch: 0.6937\n",
      "Epoch 23: accuracy on validation set: 0.4977\n",
      "Epoch 23: accuracy on training set: 0.4920\n",
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      "Epoch 24: accuracy on validation set: 0.4977\n",
      "Epoch 24: accuracy on training set: 0.4920\n",
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      "Epoch 32: loss on final training batch: 0.6953\n",
      "Epoch 32: accuracy on validation set: 0.4977\n",
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      "Epoch 33: loss on final training batch: 0.6899\n",
      "Epoch 33: accuracy on validation set: 0.4977\n",
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      "Epoch 47: accuracy on validation set: 0.4977\n",
      "Epoch 47: accuracy on training set: 0.4920\n",
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      "Epoch 49: loss on final training batch: 0.6904\n",
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      "Epoch 49: accuracy on training set: 0.4920\n",
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      "Epoch 50: loss on final training batch: 0.6932\n",
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      "Epoch 51: accuracy on validation set: 0.4977\n",
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      "Epoch 52: loss on final training batch: 0.6937\n",
      "Epoch 52: accuracy on validation set: 0.4977\n",
      "Epoch 52: accuracy on training set: 0.4920\n",
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      "Epoch 53: loss on final training batch: 0.6936\n",
      "Epoch 53: accuracy on validation set: 0.4977\n",
      "Epoch 53: accuracy on training set: 0.4920\n",
      "Epoch 53: loss on validation set: 0.6932\n",
      "Epoch 54: loss on final training batch: 0.6931\n",
      "Epoch 54: accuracy on validation set: 0.4977\n",
      "Epoch 54: accuracy on training set: 0.4920\n",
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      "Epoch 55: accuracy on validation set: 0.4977\n",
      "Epoch 55: accuracy on training set: 0.4920\n",
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      "Epoch 56: accuracy on validation set: 0.4977\n",
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      "Epoch 57: loss on final training batch: 0.6929\n",
      "Epoch 57: accuracy on validation set: 0.4977\n",
      "Epoch 57: accuracy on training set: 0.4920\n",
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      "Epoch 58: loss on final training batch: 0.6925\n",
      "Epoch 58: accuracy on validation set: 0.4977\n",
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      "Epoch 58: loss on validation set: 0.6931\n",
      "Epoch 59: loss on final training batch: 0.6930\n",
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      "Epoch 61: accuracy on validation set: 0.4977\n",
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      "Epoch 62: accuracy on validation set: 0.4977\n",
      "Epoch 62: accuracy on training set: 0.4920\n",
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      "Epoch 63: loss on final training batch: 0.6904\n",
      "Epoch 63: accuracy on validation set: 0.4977\n",
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      "Epoch 64: loss on final training batch: 0.6920\n",
      "Epoch 64: accuracy on validation set: 0.4977\n",
      "Epoch 64: accuracy on training set: 0.4920\n",
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      "Epoch 65: loss on final training batch: 0.6907\n",
      "Epoch 65: accuracy on validation set: 0.4977\n",
      "Epoch 65: accuracy on training set: 0.4920\n",
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      "Epoch 66: accuracy on validation set: 0.4977\n",
      "Epoch 66: accuracy on training set: 0.4920\n",
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      "Epoch 67: loss on final training batch: 0.6939\n",
      "Epoch 67: accuracy on validation set: 0.4977\n",
      "Epoch 67: accuracy on training set: 0.4920\n",
      "Epoch 67: loss on validation set: 0.6929\n",
      "Epoch 68: loss on final training batch: 0.6916\n",
      "Epoch 68: accuracy on validation set: 0.4977\n",
      "Epoch 68: accuracy on training set: 0.4920\n",
      "Epoch 68: loss on validation set: 0.6929\n",
      "Epoch 69: loss on final training batch: 0.6932\n",
      "Epoch 69: accuracy on validation set: 0.4977\n",
      "Epoch 69: accuracy on training set: 0.4920\n",
      "Epoch 69: loss on validation set: 0.6928\n",
      "Epoch 70: loss on final training batch: 0.6892\n",
      "Epoch 70: accuracy on validation set: 0.4977\n",
      "Epoch 70: accuracy on training set: 0.4920\n",
      "Epoch 70: loss on validation set: 0.6927\n",
      "Epoch 71: loss on final training batch: 0.6901\n",
      "Epoch 71: accuracy on validation set: 0.4977\n",
      "Epoch 71: accuracy on training set: 0.4920\n",
      "Epoch 71: loss on validation set: 0.6926\n",
      "Epoch 72: loss on final training batch: 0.6924\n",
      "Epoch 72: accuracy on validation set: 0.4977\n",
      "Epoch 72: accuracy on training set: 0.4920\n",
      "Epoch 72: loss on validation set: 0.6925\n",
      "Epoch 73: loss on final training batch: 0.6923\n",
      "Epoch 73: accuracy on validation set: 0.4977\n",
      "Epoch 73: accuracy on training set: 0.4920\n",
      "Epoch 73: loss on validation set: 0.6924\n",
      "Epoch 74: loss on final training batch: 0.6914\n",
      "Epoch 74: accuracy on validation set: 0.4977\n",
      "Epoch 74: accuracy on training set: 0.4920\n",
      "Epoch 74: loss on validation set: 0.6922\n",
      "Epoch 75: loss on final training batch: 0.6923\n",
      "Epoch 75: accuracy on validation set: 0.4977\n",
      "Epoch 75: accuracy on training set: 0.4920\n",
      "Epoch 75: loss on validation set: 0.6919\n",
      "Epoch 76: loss on final training batch: 0.6907\n",
      "Epoch 76: accuracy on validation set: 0.4981\n",
      "Epoch 76: accuracy on training set: 0.4916\n",
      "Epoch 76: loss on validation set: 0.6916\n",
      "Epoch 77: loss on final training batch: 0.6884\n",
      "Epoch 77: accuracy on validation set: 0.5040\n",
      "Epoch 77: accuracy on training set: 0.4955\n",
      "Epoch 77: loss on validation set: 0.6912\n",
      "Epoch 78: loss on final training batch: 0.6877\n",
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      "Epoch 480: accuracy on validation set: 0.5142\n",
      "Epoch 480: accuracy on training set: 0.7064\n",
      "Epoch 480: loss on validation set: 0.8111\n",
      "Epoch 481: loss on final training batch: 0.2482\n",
      "Epoch 481: accuracy on validation set: 0.5142\n",
      "Epoch 481: accuracy on training set: 0.7063\n",
      "Epoch 481: loss on validation set: 0.8119\n",
      "Epoch 482: loss on final training batch: 0.2362\n",
      "Epoch 482: accuracy on validation set: 0.5100\n",
      "Epoch 482: accuracy on training set: 0.7087\n",
      "Epoch 482: loss on validation set: 0.8127\n",
      "Epoch 483: loss on final training batch: 0.2608\n",
      "Epoch 483: accuracy on validation set: 0.5106\n",
      "Epoch 483: accuracy on training set: 0.7059\n",
      "Epoch 483: loss on validation set: 0.8138\n",
      "Epoch 484: loss on final training batch: 0.2282\n",
      "Epoch 484: accuracy on validation set: 0.5104\n",
      "Epoch 484: accuracy on training set: 0.7165\n",
      "Epoch 484: loss on validation set: 0.8137\n",
      "Epoch 485: loss on final training batch: 0.2426\n",
      "Epoch 485: accuracy on validation set: 0.5108\n",
      "Epoch 485: accuracy on training set: 0.7075\n",
      "Epoch 485: loss on validation set: 0.8138\n",
      "Epoch 486: loss on final training batch: 0.2146\n",
      "Epoch 486: accuracy on validation set: 0.5110\n",
      "Epoch 486: accuracy on training set: 0.7118\n",
      "Epoch 486: loss on validation set: 0.8153\n",
      "Epoch 487: loss on final training batch: 0.2401\n",
      "Epoch 487: accuracy on validation set: 0.5123\n",
      "Epoch 487: accuracy on training set: 0.7082\n",
      "Epoch 487: loss on validation set: 0.8146\n",
      "Epoch 488: loss on final training batch: 0.2428\n",
      "Epoch 488: accuracy on validation set: 0.5119\n",
      "Epoch 488: accuracy on training set: 0.7047\n",
      "Epoch 488: loss on validation set: 0.8151\n",
      "Epoch 489: loss on final training batch: 0.2315\n",
      "Epoch 489: accuracy on validation set: 0.5117\n",
      "Epoch 489: accuracy on training set: 0.7104\n",
      "Epoch 489: loss on validation set: 0.8137\n",
      "Epoch 490: loss on final training batch: 0.2466\n",
      "Epoch 490: accuracy on validation set: 0.5133\n",
      "Epoch 490: accuracy on training set: 0.7117\n",
      "Epoch 490: loss on validation set: 0.8134\n",
      "Epoch 491: loss on final training batch: 0.2193\n",
      "Epoch 491: accuracy on validation set: 0.5125\n",
      "Epoch 491: accuracy on training set: 0.7099\n",
      "Epoch 491: loss on validation set: 0.8129\n",
      "Epoch 492: loss on final training batch: 0.2588\n",
      "Epoch 492: accuracy on validation set: 0.5113\n",
      "Epoch 492: accuracy on training set: 0.7103\n",
      "Epoch 492: loss on validation set: 0.8149\n",
      "Epoch 493: loss on final training batch: 0.2472\n",
      "Epoch 493: accuracy on validation set: 0.5113\n",
      "Epoch 493: accuracy on training set: 0.7122\n",
      "Epoch 493: loss on validation set: 0.8137\n",
      "Epoch 494: loss on final training batch: 0.2245\n",
      "Epoch 494: accuracy on validation set: 0.5113\n",
      "Epoch 494: accuracy on training set: 0.7132\n",
      "Epoch 494: loss on validation set: 0.8151\n",
      "Epoch 495: loss on final training batch: 0.2452\n",
      "Epoch 495: accuracy on validation set: 0.5138\n",
      "Epoch 495: accuracy on training set: 0.7118\n",
      "Epoch 495: loss on validation set: 0.8169\n",
      "Epoch 496: loss on final training batch: 0.2340\n",
      "Epoch 496: accuracy on validation set: 0.5129\n",
      "Epoch 496: accuracy on training set: 0.7165\n",
      "Epoch 496: loss on validation set: 0.8174\n",
      "Epoch 497: loss on final training batch: 0.2245\n",
      "Epoch 497: accuracy on validation set: 0.5096\n",
      "Epoch 497: accuracy on training set: 0.7108\n",
      "Epoch 497: loss on validation set: 0.8187\n",
      "Epoch 498: loss on final training batch: 0.2467\n",
      "Epoch 498: accuracy on validation set: 0.5113\n",
      "Epoch 498: accuracy on training set: 0.7165\n",
      "Epoch 498: loss on validation set: 0.8191\n",
      "Epoch 499: loss on final training batch: 0.2049\n",
      "Epoch 499: accuracy on validation set: 0.5165\n",
      "Epoch 499: accuracy on training set: 0.7113\n",
      "Epoch 499: loss on validation set: 0.8171\n",
      "Epoch 500: loss on final training batch: 0.2189\n",
      "Epoch 500: accuracy on validation set: 0.5140\n",
      "Epoch 500: accuracy on training set: 0.7099\n",
      "Epoch 500: loss on validation set: 0.8184\n"
     ]
    }
   ],
   "source": [
    "model2 = torch.nn.Sequential(\n",
    "    torch.nn.Linear(276,128),\n",
    "    #torch.nn.BatchNorm1d(128),\n",
    "    torch.nn.Dropout(),\n",
    "    torch.nn.ReLU(),\n",
    "    \n",
    "    torch.nn.Linear(128,64),\n",
    "    torch.nn.Dropout(),\n",
    "    torch.nn.ReLU(),\n",
    "    # torch.nn.Dropout(p=0.2),\n",
    "    torch.nn.Linear(64,32),\n",
    "    torch.nn.Dropout(),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(32,16),\n",
    "    torch.nn.Dropout(),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(16,1)\n",
    ")\n",
    "\n",
    "if torch.cuda.is_available(): \n",
    " dev = \"cuda:0\" \n",
    "else: \n",
    " dev = \"cpu\" \n",
    "device = torch.device(dev)\n",
    "\n",
    "model2.to(device)\n",
    "\n",
    "\n",
    "#loss function and optimizer\n",
    "loss = torch.nn.BCEWithLogitsLoss()\n",
    "optim = torch.optim.Adam(model2.parameters(),lr=1e-4, weight_decay=1e-3)\n",
    "\n",
    "#epoch number\n",
    "num_epoch = 500\n",
    "next_epoch = 1\n",
    "batch_size = 1000\n",
    "\n",
    "valid_loss = 100000000000\n",
    "loss_flag = False\n",
    "\n",
    "#training loop\n",
    "for epoch in range(next_epoch, next_epoch+num_epoch):\n",
    "    model2.train()\n",
    "    \n",
    "    \n",
    "    # Make an entire pass (an 'epoch') over the training data in batch_size chunks\n",
    "    for i in range(0, len(X_train), batch_size):        \n",
    "        X = X_train[i:i+batch_size].to(device)     # Slice out a mini-batch of features\n",
    "        y = y_train[i:i+batch_size].to(device)     # Slice out a mini-batch of targets\n",
    "        \n",
    "        \n",
    "\n",
    "        y_pred = model2(X)                   # Make predictions (final-layer activations)\n",
    "        \n",
    "        l = loss(y_pred, y)                 # Compute loss with respect to predictions\n",
    "        \n",
    "        model2.zero_grad()                   # Reset all gradient accumulators to zero (PyTorch thing)\n",
    "        l.backward()                        # Compute gradient of loss wrt all parameters (backprop!)\n",
    "        optim.step()                    # Use the gradients to take a step with SGD.\n",
    "        \n",
    "    print(\"Epoch %2d: loss on final training batch: %.4f\" % (epoch, l.item()))\n",
    "    # pred = torch.sign(model(X_val))\n",
    "    # acc = torch.mean((pred == y_val).float())\n",
    "    \n",
    "    model2.eval()\n",
    "\n",
    "    #create dire query\n",
    "    \n",
    "    #validation set calculations\n",
    "    dire_X =  torch.index_select(X_val, 1, torch.LongTensor([*range(138,276)]))\n",
    "    dire_X = torch.cat((dire_X,torch.index_select(X_val, 1, torch.LongTensor([*range(0,138)]))),1).to(device)\n",
    "\n",
    "    dire_pred = (model2(dire_X) >= 0).float()\n",
    "    \n",
    "    \n",
    "    rad_pred = (model2(X_val.to(device)) >= 0).float()\n",
    "    \n",
    "\n",
    "    overall_prob = (((rad_pred + (1 - dire_pred))/2) > 0.5).float()\n",
    "    \n",
    "    val_acc = torch.mean((overall_prob.to('cpu') == y_val.to('cpu')).float())\n",
    "\n",
    "    #training set calculations\n",
    "    dire_X_train =  torch.index_select(X_train, 1, torch.LongTensor([*range(138,276)]))\n",
    "    dire_X_train = torch.cat((dire_X_train,torch.index_select(X_train, 1, torch.LongTensor([*range(0,138)]))),1).to(device)\n",
    "\n",
    "    dire_pred = (model2(dire_X_train) >= 0).float()\n",
    "    \n",
    "    rad_pred = (model2(X_train.to(device)) >= 0).float()\n",
    "    \n",
    "\n",
    "    overall_prob = (((rad_pred + (1 - dire_pred))/2) > 0.5).float()\n",
    "    \n",
    "    train_acc = torch.mean((overall_prob.to('cpu') == y_train.to('cpu')).float())\n",
    "    \n",
    "    print(\"Epoch %2d: accuracy on validation set: %.4f\" % (epoch, val_acc))\n",
    "    print(\"Epoch %2d: accuracy on training set: %.4f\" % (epoch, train_acc))\n",
    "    \n",
    "    print(\"Epoch %2d: loss on validation set: %.4f\" % (epoch, loss(model2(X_val.to(device)), y_val.to(device))))\n",
    "    new_val_loss = loss(model2(X_val.to(device)), y_val.to(device))\n",
    "\n",
    "    if new_val_loss > valid_loss:\n",
    "       pass\n",
    "\n",
    "    valid_loss = new_val_loss\n",
    "    \n",
    "   \n",
    "\n",
    "next_epoch = epoch+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy:  50.64062499999999 %\n",
      "held-out accuracy (testing):  50.9875 %\n",
      "log training accuracy:  58.203125 %\n",
      "log held-out accuracy (testing):  57.037499999999994 %\n",
      "log overall accuracy:  53.82499999999999 %\n",
      "held-out accuracy (2-fold):   54.1%\n",
      "held-out accuracy (3-fold):   55.1%\n",
      "held-out accuracy (4-fold):   55.3%\n",
      "held-out accuracy (5-fold):   55.8%\n",
      "held-out accuracy (6-fold):   55.8%\n",
      "held-out accuracy (7-fold):   55.5%\n",
      "held-out accuracy (8-fold):   55.6%\n",
      "held-out accuracy (9-fold):   55.6%\n",
      "held-out accuracy (10-fold):   55.6%\n",
      "held-out accuracy (11-fold):   55.8%\n",
      "held-out accuracy (12-fold):   55.7%\n",
      "held-out accuracy (13-fold):   55.6%\n",
      "held-out accuracy (14-fold):   55.8%\n",
      "held-out accuracy (15-fold):   55.5%\n",
      "held-out accuracy (16-fold):   55.6%\n",
      "held-out accuracy (17-fold):   55.5%\n",
      "held-out accuracy (18-fold):   55.5%\n",
      "held-out accuracy (19-fold):   55.4%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X_val_trn,y_data, test_size = 0.2, shuffle= True)\n",
    "\n",
    "mlp = MLPClassifier(hidden_layer_sizes = 4,activation='relu',solver='adam',batch_size=16,learning_rate_init=0.05,max_iter=3000,random_state=0)# Your code here\n",
    "log = LogisticRegression(max_iter=300)\n",
    "mlp.fit(X_train,y_train.ravel())\n",
    "log.fit(X_train,y_train.ravel())\n",
    "\n",
    "print('training accuracy: ',mlp.score(X_train,y_train)*100,'%')\n",
    "print('held-out accuracy (testing): ',mlp.score(X_test,y_test)*100,'%')\n",
    "\n",
    "dire_X =  torch.index_select(X_test, 1, torch.LongTensor([*range(138,276)]))\n",
    "dire_X = torch.cat((dire_X, torch.index_select(X_test, 1, torch.LongTensor([*range(0,138)]))),1)\n",
    "\n",
    "\n",
    "\n",
    "dire_prob = log.score(dire_X,y_test)\n",
    "rad_prob = log.score(X_test,y_test)\n",
    "\n",
    "overall_prob = ((rad_prob + (1 - dire_prob))/2)\n",
    "\n",
    "print('log training accuracy: ',log.score(X_train,y_train)*100,'%')\n",
    "print('log held-out accuracy (testing): ',log.score(X_test,y_test)*100,'%')\n",
    "\n",
    "print('log overall accuracy: ',overall_prob*100,'%')\n",
    "\n",
    "for k in range(2,10):\n",
    "    scores = sklearn.model_selection.cross_val_score(log,X_test,y_test.ravel(),cv=k) \n",
    "    print(\"held-out accuracy (%d-fold):   %.1f%%\" % (k, scores.mean()*100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.520, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "#test accuracy on test set \n",
    "dire_X =  torch.index_select(X_test, 1, torch.LongTensor([*range(138,276)]))\n",
    "dire_X = torch.cat((dire_X,torch.index_select(X_test, 1, torch.LongTensor([*range(0,138)]))),1).to(device)\n",
    "dire_pred = (model2(dire_X) >= 0).float()\n",
    "rad_pred = (model2(X_test.to(device)) >= 0).float()\n",
    "\n",
    "\n",
    "overall_prob = (((rad_pred + (1 - dire_pred))/2) > 0.5).float()\n",
    "acc = torch.mean((overall_prob.to(device) == y_test.to(device)).float())\n",
    "print(acc)"
   ]
  }
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