{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Importing the Required Libraries"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import torch\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import preprocessing",
   "execution_count": 1,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Import the Data"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "dataset = pd.read_csv('mlb_games_overview.csv')\ndataset.head()",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>team</th>\n      <th>away</th>\n      <th>at_bats</th>\n      <th>runs</th>\n      <th>hits</th>\n      <th>rbis</th>\n      <th>walks</th>\n      <th>batting_average</th>\n      <th>on_base_percentage</th>\n      <th>slugging_percentage</th>\n      <th>win</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>20190320</td>\n      <td>SEA</td>\n      <td>1</td>\n      <td>31</td>\n      <td>9</td>\n      <td>7</td>\n      <td>9</td>\n      <td>6</td>\n      <td>0.163</td>\n      <td>0.438</td>\n      <td>0.317</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>20190320</td>\n      <td>OAK</td>\n      <td>0</td>\n      <td>9</td>\n      <td>7</td>\n      <td>6</td>\n      <td>3</td>\n      <td>8</td>\n      <td>0.263</td>\n      <td>0.304</td>\n      <td>0.496</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>20190320</td>\n      <td>DET</td>\n      <td>1</td>\n      <td>29</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>4</td>\n      <td>0.180</td>\n      <td>0.270</td>\n      <td>0.303</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>20190320</td>\n      <td>PHI</td>\n      <td>0</td>\n      <td>30</td>\n      <td>1</td>\n      <td>6</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0.157</td>\n      <td>0.213</td>\n      <td>0.263</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>20190320</td>\n      <td>MIA</td>\n      <td>1</td>\n      <td>38</td>\n      <td>6</td>\n      <td>11</td>\n      <td>5</td>\n      <td>5</td>\n      <td>0.198</td>\n      <td>0.244</td>\n      <td>0.310</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "       date team  away  at_bats  runs  hits  rbis  walks  batting_average  \\\n0  20190320  SEA     1       31     9     7     9      6            0.163   \n1  20190320  OAK     0        9     7     6     3      8            0.263   \n2  20190320  DET     1       29     3     4     3      4            0.180   \n3  20190320  PHI     0       30     1     6     0      2            0.157   \n4  20190320  MIA     1       38     6    11     5      5            0.198   \n\n   on_base_percentage  slugging_percentage  win  \n0               0.438                0.317    1  \n1               0.304                0.496    0  \n2               0.270                0.303    1  \n3               0.213                0.263    0  \n4               0.244                0.310    1  "
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Split the Data & Convert it to Tensors"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "min_max_scaler_X = preprocessing.MinMaxScaler()\nmin_max_scaler_y = preprocessing.MinMaxScaler()\n\nX = dataset.drop(['date', 'team', 'runs', 'win'], 1)\nvalues_X = X.to_numpy()\nscaled_values_X = min_max_scaler_X.fit_transform(values_X)\nX = pd.DataFrame(scaled_values_X)\n\ny = dataset[['win']]\nvalues_y = y.to_numpy()\nscaled_values_y = min_max_scaler_y.fit_transform(values_y)\ny = pd.DataFrame(scaled_values_y)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=True)\n\nX_train = torch.as_tensor(X_train.to_numpy(), dtype=torch.float32)\nX_test = torch.as_tensor(X_test.to_numpy(), dtype=torch.float32)\ny_train = torch.as_tensor(y_train.to_numpy(), dtype=torch.float32)\ny_test = torch.as_tensor(y_test.to_numpy(), dtype=torch.float32)",
   "execution_count": 3,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "print(X_train[0])\nprint(y_train[0])",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "tensor([1.0000, 0.4783, 0.2593, 0.2500, 0.0870, 0.7340, 0.7092, 0.6286])\ntensor([0.])\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Crate the Model"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "class LinearRegressionModel(torch.nn.Module):\n    def __init__(self):\n        super(LinearRegressionModel, self).__init__()\n        self.linear = torch.nn.Linear(8, 1)\n        self.sigmoid = torch.nn.Sigmoid()\n    def forward(self, x):\n        y_pred = self.sigmoid(self.linear(x))\n        return y_pred",
   "execution_count": 5,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Declare an Instance of the Model"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "model = LinearRegressionModel()",
   "execution_count": 6,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Initialize Optimizer and Loss Criteria"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "criterion = torch.nn.MSELoss(reduction='sum')\noptimizer = torch.optim.SGD(model.parameters(), lr=0.001)",
   "execution_count": 7,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Training the Model"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "EPOCHS = 1000\nfor epoch in range(EPOCHS):\n    pred_y = model(X_train)\n    loss = criterion(pred_y, y_train)\n    optimizer.zero_grad() # zero out gradients to update parameters correctly\n    loss.backward() # backpropagation\n    optimizer.step() # update weights\n    print('epoch {}, loss {}'. format(epoch, loss.data))",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "epoch 0, loss 909.458984375\nepoch 1, loss 899.5485229492188\nepoch 2, loss 894.7926025390625\nepoch 3, loss 890.13037109375\nepoch 4, loss 885.5618896484375\nepoch 5, loss 881.0872192382812\nepoch 6, loss 876.7061767578125\nepoch 7, loss 872.4183959960938\nepoch 8, loss 868.223388671875\nepoch 9, loss 864.1204833984375\nepoch 10, loss 860.1088256835938\nepoch 11, loss 856.1874389648438\nepoch 12, loss 852.355224609375\nepoch 13, loss 848.6109619140625\nepoch 14, loss 844.9534301757812\nepoch 15, loss 841.381103515625\nepoch 16, loss 837.892578125\nepoch 17, loss 834.486328125\nepoch 18, loss 831.1607666015625\nepoch 19, loss 827.9141845703125\nepoch 20, loss 824.7450561523438\nepoch 21, loss 821.6515502929688\nepoch 22, loss 818.632080078125\nepoch 23, loss 815.684814453125\nepoch 24, loss 812.8081665039062\nepoch 25, loss 810.0003662109375\nepoch 26, loss 807.2597045898438\nepoch 27, loss 804.5845336914062\nepoch 28, loss 801.9730834960938\nepoch 29, loss 799.423828125\nepoch 30, loss 796.9351196289062\nepoch 31, loss 794.5052490234375\nepoch 32, loss 792.1328125\nepoch 33, loss 789.816162109375\nepoch 34, loss 787.5537719726562\nepoch 35, loss 785.34423828125\nepoch 36, loss 783.1860961914062\nepoch 37, loss 781.077880859375\nepoch 38, loss 779.018310546875\nepoch 39, loss 777.0059814453125\nepoch 40, loss 775.0396118164062\nepoch 41, loss 773.1179809570312\nepoch 42, loss 771.23974609375\nepoch 43, loss 769.4037475585938\nepoch 44, loss 767.60888671875\nepoch 45, loss 765.8539428710938\nepoch 46, loss 764.1378173828125\nepoch 47, loss 762.4595336914062\nepoch 48, loss 760.8179321289062\nepoch 49, loss 759.2120971679688\nepoch 50, loss 757.6409912109375\nepoch 51, loss 756.1036376953125\nepoch 52, loss 754.5992431640625\nepoch 53, loss 753.1268310546875\nepoch 54, loss 751.6854858398438\nepoch 55, loss 750.2744750976562\nepoch 56, loss 748.8929443359375\nepoch 57, loss 747.5400390625\nepoch 58, loss 746.215087890625\nepoch 59, loss 744.9172973632812\nepoch 60, loss 743.64599609375\nepoch 61, loss 742.400390625\nepoch 62, loss 741.179931640625\nepoch 63, loss 739.98388671875\nepoch 64, loss 738.8116455078125\nepoch 65, loss 737.66259765625\nepoch 66, loss 736.5360717773438\nepoch 67, loss 735.4315795898438\nepoch 68, loss 734.3485717773438\nepoch 69, loss 733.2864379882812\nepoch 70, loss 732.24462890625\nepoch 71, loss 731.2227172851562\nepoch 72, loss 730.22021484375\nepoch 73, loss 729.2365112304688\nepoch 74, loss 728.2713012695312\nepoch 75, loss 727.323974609375\nepoch 76, loss 726.3942260742188\nepoch 77, loss 725.4815063476562\nepoch 78, loss 724.5855102539062\nepoch 79, loss 723.705810546875\nepoch 80, loss 722.8419799804688\nepoch 81, loss 721.99365234375\nepoch 82, loss 721.1604614257812\nepoch 83, loss 720.342041015625\nepoch 84, loss 719.5380249023438\nepoch 85, loss 718.7481689453125\nepoch 86, loss 717.9720458984375\nepoch 87, loss 717.2094116210938\nepoch 88, loss 716.4598388671875\nepoch 89, loss 715.7232055664062\nepoch 90, loss 714.9990234375\nepoch 91, loss 714.2871704101562\nepoch 92, loss 713.5872802734375\nepoch 93, loss 712.8991088867188\nepoch 94, loss 712.222412109375\nepoch 95, loss 711.5569458007812\nepoch 96, loss 710.9024047851562\nepoch 97, loss 710.258544921875\nepoch 98, loss 709.625244140625\nepoch 99, loss 709.0021362304688\nepoch 100, loss 708.3890991210938\nepoch 101, loss 707.7858276367188\nepoch 102, loss 707.1921997070312\nepoch 103, loss 706.6079711914062\nepoch 104, loss 706.032958984375\nepoch 105, loss 705.4669799804688\nepoch 106, loss 704.9097900390625\nepoch 107, loss 704.3612670898438\nepoch 108, loss 703.8211669921875\nepoch 109, loss 703.2893676757812\nepoch 110, loss 702.7656860351562\nepoch 111, loss 702.25\nepoch 112, loss 701.7420654296875\nepoch 113, loss 701.24169921875\nepoch 114, loss 700.7489013671875\nepoch 115, loss 700.263427734375\nepoch 116, loss 699.7850952148438\nepoch 117, loss 699.3138427734375\nepoch 118, loss 698.8494873046875\nepoch 119, loss 698.391845703125\nepoch 120, loss 697.94091796875\nepoch 121, loss 697.4964599609375\nepoch 122, loss 697.058349609375\nepoch 123, loss 696.6265258789062\nepoch 124, loss 696.2008666992188\nepoch 125, loss 695.78125\nepoch 126, loss 695.3674926757812\nepoch 127, loss 694.9595336914062\nepoch 128, loss 694.5572509765625\nepoch 129, loss 694.1605834960938\nepoch 130, loss 693.7694091796875\nepoch 131, loss 693.3836059570312\nepoch 132, loss 693.0030517578125\nepoch 133, loss 692.627685546875\nepoch 134, loss 692.2574462890625\nepoch 135, loss 691.8921508789062\nepoch 136, loss 691.5317993164062\nepoch 137, loss 691.17626953125\nepoch 138, loss 690.825439453125\nepoch 139, loss 690.479248046875\nepoch 140, loss 690.1376342773438\nepoch 141, loss 689.800537109375\nepoch 142, loss 689.4678344726562\nepoch 143, loss 689.139404296875\nepoch 144, loss 688.8153076171875\nepoch 145, loss 688.495361328125\nepoch 146, loss 688.1795043945312\nepoch 147, loss 687.86767578125\nepoch 148, loss 687.559814453125\nepoch 149, loss 687.2559204101562\nepoch 150, loss 686.955810546875\nepoch 151, loss 686.6594848632812\nepoch 152, loss 686.3668212890625\nepoch 153, loss 686.0778198242188\nepoch 154, loss 685.79248046875\nepoch 155, loss 685.5105590820312\nepoch 156, loss 685.232177734375\nepoch 157, loss 684.9571533203125\nepoch 158, loss 684.685546875\nepoch 159, loss 684.4171752929688\nepoch 160, loss 684.152099609375\nepoch 161, loss 683.8901977539062\nepoch 162, loss 683.6314086914062\nepoch 163, loss 683.375732421875\nepoch 164, loss 683.1231079101562\nepoch 165, loss 682.8734741210938\nepoch 166, loss 682.6268310546875\nepoch 167, loss 682.3829956054688\nepoch 168, loss 682.14208984375\nepoch 169, loss 681.9039916992188\nepoch 170, loss 681.6686401367188\nepoch 171, loss 681.4359741210938\nepoch 172, loss 681.2060546875\nepoch 173, loss 680.978759765625\nepoch 174, loss 680.7540283203125\nepoch 175, loss 680.5319213867188\nepoch 176, loss 680.312255859375\nepoch 177, loss 680.0951538085938\nepoch 178, loss 679.8804321289062\nepoch 179, loss 679.6681518554688\nepoch 180, loss 679.458251953125\nepoch 181, loss 679.2506713867188\nepoch 182, loss 679.04541015625\nepoch 183, loss 678.8424072265625\nepoch 184, loss 678.6416625976562\nepoch 185, loss 678.443115234375\nepoch 186, loss 678.2467041015625\nepoch 187, loss 678.052490234375\nepoch 188, loss 677.8603515625\nepoch 189, loss 677.6702880859375\nepoch 190, loss 677.4822998046875\nepoch 191, loss 677.2962646484375\nepoch 192, loss 677.1123046875\nepoch 193, loss 676.9302368164062\nepoch 194, loss 676.7501831054688\nepoch 195, loss 676.5719604492188\nepoch 196, loss 676.3956298828125\nepoch 197, loss 676.22119140625\nepoch 198, loss 676.048583984375\nepoch 199, loss 675.8777465820312\nepoch 200, loss 675.7086791992188\nepoch 201, loss 675.5413818359375\nepoch 202, loss 675.3757934570312\nepoch 203, loss 675.2119140625\nepoch 204, loss 675.0497436523438\nepoch 205, loss 674.8892211914062\nepoch 206, loss 674.7303466796875\nepoch 207, loss 674.5730590820312\nepoch 208, loss 674.4174194335938\nepoch 209, loss 674.2633056640625\nepoch 210, loss 674.1107177734375\nepoch 211, loss 673.959716796875\nepoch 212, loss 673.8101806640625\nepoch 213, loss 673.6621704101562\nepoch 214, loss 673.5155639648438\nepoch 215, loss 673.3704833984375\nepoch 216, loss 673.226806640625\nepoch 217, loss 673.0845336914062\nepoch 218, loss 672.9436645507812\nepoch 219, loss 672.80419921875\nepoch 220, loss 672.666015625\nepoch 221, loss 672.5292358398438\nepoch 222, loss 672.393798828125\nepoch 223, loss 672.2595825195312\nepoch 224, loss 672.126708984375\nepoch 225, loss 671.9951171875\nepoch 226, loss 671.8648071289062\nepoch 227, loss 671.7357177734375\nepoch 228, loss 671.6077880859375\nepoch 229, loss 671.4811401367188\nepoch 230, loss 671.3556518554688\nepoch 231, loss 671.2313842773438\nepoch 232, loss 671.1082763671875\nepoch 233, loss 670.986328125\nepoch 234, loss 670.865478515625\nepoch 235, loss 670.7457885742188\nepoch 236, loss 670.6271362304688\nepoch 237, loss 670.5096435546875\nepoch 238, loss 670.3932495117188\nepoch 239, loss 670.2778930664062\nepoch 240, loss 670.16357421875\nepoch 241, loss 670.0503540039062\nepoch 242, loss 669.9381103515625\nepoch 243, loss 669.826904296875\nepoch 244, loss 669.7167358398438\nepoch 245, loss 669.6075439453125\nepoch 246, loss 669.4993286132812\nepoch 247, loss 669.39208984375\nepoch 248, loss 669.2857666015625\nepoch 249, loss 669.180419921875\nepoch 250, loss 669.0760498046875\nepoch 251, loss 668.9725952148438\nepoch 252, loss 668.8699951171875\nepoch 253, loss 668.7683715820312\nepoch 254, loss 668.6676025390625\nepoch 255, loss 668.5677490234375\nepoch 256, loss 668.46875\nepoch 257, loss 668.37060546875\nepoch 258, loss 668.2733154296875\nepoch 259, loss 668.1768798828125\nepoch 260, loss 668.081298828125\nepoch 261, loss 667.9865112304688\nepoch 262, loss 667.8925170898438\nepoch 263, loss 667.7993774414062\nepoch 264, loss 667.70703125\nepoch 265, loss 667.6154174804688\nepoch 266, loss 667.524658203125\nepoch 267, loss 667.4346313476562\nepoch 268, loss 667.3453369140625\nepoch 269, loss 667.2568359375\nepoch 270, loss 667.1690673828125\nepoch 271, loss 667.0820922851562\nepoch 272, loss 666.9957885742188\nepoch 273, loss 666.9102172851562\nepoch 274, loss 666.8253173828125\nepoch 275, loss 666.7411499023438\nepoch 276, loss 666.65771484375\nepoch 277, loss 666.574951171875\nepoch 278, loss 666.4928588867188\nepoch 279, loss 666.4114379882812\nepoch 280, loss 666.3306884765625\nepoch 281, loss 666.2506103515625\nepoch 282, loss 666.171142578125\nepoch 283, loss 666.0924072265625\nepoch 284, loss 666.0142211914062\nepoch 285, loss 665.9367065429688\nepoch 286, loss 665.85986328125\nepoch 287, loss 665.7835693359375\nepoch 288, loss 665.7079467773438\nepoch 289, loss 665.6328735351562\nepoch 290, loss 665.5584106445312\nepoch 291, loss 665.4845581054688\nepoch 292, loss 665.4113159179688\nepoch 293, loss 665.338623046875\nepoch 294, loss 665.2664794921875\nepoch 295, loss 665.1949462890625\nepoch 296, loss 665.1239624023438\nepoch 297, loss 665.0535888671875\nepoch 298, loss 664.9837036132812\nepoch 299, loss 664.9143676757812\nepoch 300, loss 664.8455810546875\nepoch 301, loss 664.77734375\nepoch 302, loss 664.7095947265625\nepoch 303, loss 664.6424560546875\nepoch 304, loss 664.5757446289062\nepoch 305, loss 664.5095825195312\nepoch 306, loss 664.4439697265625\nepoch 307, loss 664.3787841796875\nepoch 308, loss 664.3141479492188\nepoch 309, loss 664.25\nepoch 310, loss 664.1863403320312\nepoch 311, loss 664.1231689453125\nepoch 312, loss 664.0604248046875\nepoch 313, loss 663.9981689453125\nepoch 314, loss 663.9364624023438\nepoch 315, loss 663.8751220703125\nepoch 316, loss 663.8143310546875\nepoch 317, loss 663.75390625\nepoch 318, loss 663.6939697265625\nepoch 319, loss 663.634521484375\nepoch 320, loss 663.575439453125\nepoch 321, loss 663.516845703125\nepoch 322, loss 663.4586791992188\nepoch 323, loss 663.4009399414062\nepoch 324, loss 663.3436279296875\nepoch 325, loss 663.2867431640625\nepoch 326, loss 663.2302856445312\nepoch 327, loss 663.1741943359375\nepoch 328, loss 663.1185302734375\nepoch 329, loss 663.0632934570312\nepoch 330, loss 663.0084838867188\nepoch 331, loss 662.9539794921875\nepoch 332, loss 662.8999633789062\nepoch 333, loss 662.8463134765625\nepoch 334, loss 662.7930297851562\nepoch 335, loss 662.7401123046875\nepoch 336, loss 662.6876220703125\nepoch 337, loss 662.6354370117188\nepoch 338, loss 662.5836791992188\nepoch 339, loss 662.5322875976562\nepoch 340, loss 662.4812622070312\nepoch 341, loss 662.4306030273438\nepoch 342, loss 662.3802490234375\nepoch 343, loss 662.330322265625\nepoch 344, loss 662.2807006835938\nepoch 345, loss 662.2314453125\nepoch 346, loss 662.1825561523438\nepoch 347, loss 662.1339721679688\nepoch 348, loss 662.0857543945312\nepoch 349, loss 662.037841796875\nepoch 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