Linear_Regression_pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0a0+gitd9f822b\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'torch.DoubleTensor'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "print(torch.__version__)  \n",
    "\n",
    "# Data from House Prices: Advanced Regression Techniques\n",
    "# Cleaned Data\n",
    "X_train_pd = pd.read_csv('X_train_HP.csv')\n",
    "y_train_pd = pd.read_csv('y_train_HP.csv')\n",
    "X_cv_pd = pd.read_csv('X_cv_HP.csv')\n",
    "y_cv_pd = pd.read_csv('y_cv_HP.csv')\n",
    "test_pd = pd.read_csv('test_skew_HP.csv')\n",
    "\n",
    "# Convert to tensor and check Datatype\n",
    "\n",
    "X_t = torch.from_numpy(X_train_pd.values)\n",
    "y_t = torch.from_numpy(y_train_pd.values)\n",
    "X_cv = torch.from_numpy(X_cv_pd.values)\n",
    "y_cv = torch.from_numpy(y_cv_pd.values)\n",
    "test = torch.from_numpy(test_pd.values)\n",
    "\n",
    "X_t.type()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network\n",
    "*(Linear Regression with PyTorch)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n",
      "MSELoss|  path: torch/nn/modules/loss.py\n",
      "F.mseloss|  path: torch/nn/modules/loss.py\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Abstract_2(X, y, X_cv, y_cv, test, batch_size, epochs, lr):\n",
    "    # Model\n",
    "    model = nn.Sequential(\n",
    "        # Input Layer\n",
    "        nn.Linear(X.shape[1], 32),\n",
    "        nn.ReLU(),\n",
    "        # Hidden Layers\n",
    "        nn.Linear(32, 32),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(32, 16),\n",
    "        nn.ReLU(),\n",
    "        # Output Layer\n",
    "        nn.Linear(16, 1),\n",
    "    )\n",
    "    # Loss and Optimizer with L2 Regularization\n",
    "    criterion = nn.MSELoss()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=0.004)\n",
    "\n",
    "    # Create DataLoader for training data\n",
    "    train_data = TensorDataset(torch.Tensor(X).float(), torch.Tensor(y).float())\n",
    "    train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "    # Create DataLoader for cross validation data\n",
    "    cv_data = TensorDataset(torch.Tensor(X_cv).float(), torch.Tensor(y_cv).float())\n",
    "    cv_loader = DataLoader(cv_data, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "    # Train the Model\n",
    "    train_loss = []\n",
    "    cv_loss = []\n",
    "    for epoch in range(epochs):\n",
    "        for i, (inputs, labels) in enumerate(train_loader):\n",
    "            # Forward pass\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            print('loss: ', loss)\n",
    "            # Backward and optimize\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            # Store the loss values\n",
    "            train_loss.append(loss.item())\n",
    "\n",
    "        # Compute CV Loss\n",
    "        with torch.no_grad():\n",
    "            for inputs_cv, labels_cv in cv_loader:\n",
    "                outputs_cv = model(inputs_cv)\n",
    "                loss_cv = criterion(outputs_cv, labels_cv)\n",
    "                cv_loss.append(loss_cv.item())\n",
    "\n",
    "        # Print Loss and CV Loss every 10 epochs\n",
    "        if (epoch+1) % 10 == 0:\n",
    "            print ('Epoch [%d/%d], Train Loss: %.4f, CV Loss: %.4f' \n",
    "                   %(epoch+1, epochs, train_loss[-1], cv_loss[-1]))\n",
    "\n",
    "    # Plot the loss curve\n",
    "    plt.plot(train_loss, label='Training Loss')\n",
    "    plt.plot(cv_loss, label='Cross-Validation Loss')\n",
    "    plt.legend()\n",
    "    plt.xlim(0, epochs*len(train_loader))\n",
    "    plt.ylim(0, 1)\n",
    "    plt.xlabel('Step')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.show()\n",
    "\n",
    "    # Save the Model\n",
    "    torch.save(model.state_dict(), 'model_ab3.pkl')\n",
    "\n",
    "\n",
    "Abstract_2(X_t, y_t, X_cv, y_cv, test, 2, 1, 0.00001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L1Loss|  path: torch/nn/modules/loss.py\n",
      "F.l1_loss|  path: torch/nn/modules/loss.py\n",
      "L1Loss|  path: torch/nn/modules/loss.py\n",
      "F.l1_loss|  path: torch/nn/modules/loss.py\n"
     ]
    },
    {
     "data": {
      "image/png": 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XMgxDQ4cOlaenp3bv3q3Lly9r3LhxiomJ0a5duyRJI0eO1J133qnk5GQ5Ojrq0KFD1tnD8ePHq6SkRH//+9/l6empI0eOqFGjRlWuo7YQlgCgPiq9IL3Uwj77nnVScqkYfq71r3/9S4ZhqGPHjje02ccee0xPPfWUzevRo0dr3LhxkmSdjXrllVfUr18/5ebmyt/fX/3795ezs7Nat26tXr16Sboyy+Hp6alf//rXaty4sYKCgnTnnXded99PPfWUXn75Ze3atUv9+vWTdOUS3LBhw+Tj4yMfHx9NnTrV2n/ixInasWOHNm/efENhaefOncrOztaxY8fUqlUrSdJLL71U4T6j2bNnW39u06aNfve73yklJUXPPfec3N3d1ahRIzk5OZledtuwYYMuXryoN954wxpSX3vtNQ0aNEiLFi2Sn5+fJMnHx0evvfaaHB0d1bFjRz300EP68MMPqxWWdu7cqc8//1w5OTkKDAyUJL355pvq0qWL9u/fr7vuuku5ubmaNm2a9b+Hdu3aWdfPzc3V8OHDdccdd0iSQkJCqlxDbeIyHACgVhiGIUnWmZqfExYWZvM6Oztbffr0sWnr06ePsrOzJUm//e1vdfHiRYWEhGjMmDHatm2bLl++LEl64IEHFBQUpJCQEMXGxmrDhg26cOHKTNyGDRvUqFEj65KRkaGOHTsqIiJCa9eulSR98803ysjIsIa3srIyLVy4UN26dZOvr68aNWqktLQ05ebm3tDYsrOz1bp1a2tQkqTw8PAK/bZs2aJ77rlH/v7+atSokebMmXPD+/jpvrp3724zm9enTx+Vl5fr6NGj1rYuXbrI0dHR+jogIECnTp2q0r5+us/AwEBrUJKkzp07q0mTJtbjlZCQoGeeeUb9+/fX73//e33zzTfWvpMmTdKLL76oPn36aO7cufr888+rVUdtYWYJAOojZ48rMzz22vcNaNeunSwWi7KzszV06NCf7V/Zpbprg5ZhGNa2wMBAHT16VOnp6dq5c6fGjRunl19+Wbt371bjxo312WefadeuXUpLS9Pzzz+vefPmaf/+/Ro8eLDNbFDLli0lXbnRe8KECVqxYoXWrVunoKAg3X///ZKkJUuWaOnSpVq2bJnuuOMOeXp6Kj4+XiUlJTf0u7gaHM3G9sknn+iRRx7R/PnzFR0dLW9vb23atElLliy5oX1U9jsy2+e1N9BbLBaVl5dXaV8/t8+fts+bN0+PPfaY3nvvPb3//vuaO3euNm3apN/85jd65plnFB0drffee09paWlKSkrSkiVLNHHixGrVU9OYWQKA+shiuXIpzB7LDc4U3XbbbYqOjtaKFSv0448/Vnj/7Nmzput36tRJH3/8sU3b3r171alTJ+trd3d3DR48WMuXL9euXbuUmZmpL774QtKVe5H69++vxYsX6/PPP9exY8f0t7/9TY0bN1bbtm2ti7u7uyRpxIgRcnR01FtvvaU//elPevLJJ60n+oyMDA0ZMkSPP/64unfvrpCQEH399dc39HuQrsyy5Obm6uTJ/wu4mZmZNn327NmjoKAgJSYmKiwsTO3atdPx48dt+ri4uKisrOxn93Xo0CGb3/mePXvk4OCg9u3b33DNVXF1fCdOnLC2HTlyRIWFhTbHq3379poyZYrS0tI0bNgwrVu3zvpeYGCg4uLi9Pbbb+t3v/udVq9eXSu1VgczSwCAWrNy5UpFRESoV69eWrBggbp166bLly8rPT1dycnJ1ks0lZk2bZpGjBihnj176v7779f//u//6u2337be3Lx+/XqVlZWpd+/e8vDw0Jtvvil3d3cFBQXp3Xff1bfffqt7771XPj4+Sk1NVXl5uTp06HDd/TVq1EgxMTGaNWuWCgsLNXr0aOt7bdu21datW7V37175+Pjo1VdfVX5+vk0QMNO/f3916NBBTzzxhJYsWaKioiIlJiba9Gnbtq1yc3O1adMm3XXXXXrvvfe0bds2mz5t2rRRTk6ODh06pFatWqlx48ZydXW16TNy5EjNnTtXo0aN0rx58/TDDz9o4sSJio2Ntd6vVF1lZWU6dOiQTZuLi4v69++vbt26aeTIkVq2bJn1Bu++ffsqLCxMFy9e1LRp0/Twww8rODhY3333nfbv36/hw4dLkuLj4zVgwAC1b99e//nPf/S3v/3thn+3dcJABYWFhYYko7Cw0N6lAIBhGIZx8eJF48iRI8bFixftXUqVnTx50hg/frwRFBRkuLi4GC1btjQGDx5sfPTRR9Y+koxt27ZVWHflypVGSEiI4ezsbLRv39544403rO9t27bN6N27t+Hl5WV4enoad999t7Fz507DMAwjIyPD6Nu3r+Hj42O4u7sb3bp1M1JSUn621r179xqSjKioKJv206dPG0OGDDEaNWpkNG/e3Jg9e7bxxBNPGEOGDLH26du3rzF58mTr66CgIGPp0qXW10ePHjXuuecew8XFxWjfvr2xY8eOCuOeNm2a4evrazRq1MiIiYkxli5danh7e1vfv3TpkjF8+HCjSZMmhiRj3bp1lf7+Pv/8c6Nfv36Gm5ubcdtttxljxowxzp07Z31/1KhRNrUbhmFMnjzZ6Nu373V/N+vWrTMkVViCgoIMwzCM48ePG4MHDzY8PT2Nxo0bG7/97W+N/Px8wzAMo7i42HjkkUeMwMBAw8XFxWjRooUxYcIE63/PEyZMMG6//XbD1dXVaNasmREbG2sUFBRUWofZ30Jtnb8thlHJhdRbXFFRkby9vVVYWCgvLy97lwMAunTpknJychQcHCw3Nzd7lwPYjdnfQm2dv7lnCQAAwARhCQAAwARhCQAAwARhCQAAwARhCQDqET6Tg1udPf4GCEsAUA9cfdry1a/sAG5VV5+a/tOvaqltPJQSAOoBR0dHNWnSxPrdXR4eHjf8nWtAQ1FeXq4ffvhBHh4ecnKquwhDWAKAeuLqN81X98tOgYbAwcFBrVu3rtN/LBCWAKCesFgsCggIUPPmzVVaWmrvcgC7cHFxkYND3d5FRFgCgHrG0dGxTu/XAG513OANAABggrAEAABggrAEAABggrAEAABggrAEAABggrAEAABggrAEAABggrAEAABggrAEAABggrAEAABggrAEAABggrAEAABggrAEAABggrAEAABgwu5haeXKlQoODpabm5tCQ0OVkZFh2n/37t0KDQ2Vm5ubQkJCtGrVqgp9li1bpg4dOsjd3V2BgYGaMmWKLl26VFtDAAAADZhdw1JKSori4+OVmJiorKwsRUZGasCAAcrNza20f05OjgYOHKjIyEhlZWVp1qxZmjRpkrZu3Wrts2HDBs2YMUNz585Vdna21qxZo5SUFM2cObOuhgUAABoQi2EYhr123rt3b/Xs2VPJycnWtk6dOmno0KFKSkqq0H/69Onavn27srOzrW1xcXE6fPiwMjMzJUkTJkxQdna2PvzwQ2uf3/3ud9q3b9/PzlpdVVRUJG9vbxUWFsrLy6u6wwMAAHWots7fdptZKikp0cGDBxUVFWXTHhUVpb1791a6TmZmZoX+0dHROnDggEpLSyVJ99xzjw4ePKh9+/ZJkr799lulpqbqoYceum4txcXFKioqslkAAAAkycleOy4oKFBZWZn8/Pxs2v38/JSfn1/pOvn5+ZX2v3z5sgoKChQQEKBHHnlEP/zwg+655x4ZhqHLly/r2Wef1YwZM65bS1JSkubPn//LBwUAABocu9/gbbFYbF4bhlGh7ef6/7R9165dWrhwoVauXKnPPvtMb7/9tt5991298MIL193mzJkzVVhYaF1OnDhR3eEAAIAGxm4zS02bNpWjo2OFWaRTp05VmD26yt/fv9L+Tk5O8vX1lSTNmTNHsbGxeuaZZyRJd9xxh3788UeNHTtWiYmJcnComA9dXV3l6upaE8MCAAANjN1mllxcXBQaGqr09HSb9vT0dEVERFS6Tnh4eIX+aWlpCgsLk7OzsyTpwoULFQKRo6OjDMOQHe9lBwAA9ZRdL8MlJCToj3/8o9auXavs7GxNmTJFubm5iouLk3Tl8tgTTzxh7R8XF6fjx48rISFB2dnZWrt2rdasWaOpU6da+wwaNEjJycnatGmTcnJylJ6erjlz5mjw4MFydHSs8zECAID6zW6X4SQpJiZGp0+f1oIFC5SXl6euXbsqNTVVQUFBkqS8vDybZy4FBwcrNTVVU6ZM0YoVK9SiRQstX75cw4cPt/aZPXu2LBaLZs+ere+//17NmjXToEGDtHDhwjofHwAAqP/s+pylmxXPWQIAoP5pcM9ZAgAAqA8ISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACYISwAAACbsHpZWrlyp4OBgubm5KTQ0VBkZGab9d+/erdDQULm5uSkkJESrVq2q0Ofs2bMaP368AgIC5Obmpk6dOik1NbW2hgAAABowu4allJQUxcfHKzExUVlZWYqMjNSAAQOUm5tbaf+cnBwNHDhQkZGRysrK0qxZszRp0iRt3brV2qekpEQPPPCAjh07pi1btujo0aNavXq1WrZsWVfDAgAADYjFMAzDXjvv3bu3evbsqeTkZGtbp06dNHToUCUlJVXoP336dG3fvl3Z2dnWtri4OB0+fFiZmZmSpFWrVunll1/Wl19+KWdn52rVVVRUJG9vbxUWFsrLy6ta2wAAAHWrts7fdptZKikp0cGDBxUVFWXTHhUVpb1791a6TmZmZoX+0dHROnDggEpLSyVJ27dvV3h4uMaPHy8/Pz917dpVL730ksrKyq5bS3FxsYqKimwWAAAAyY5hqaCgQGVlZfLz87Np9/PzU35+fqXr5OfnV9r/8uXLKigokCR9++232rJli8rKypSamqrZs2dryZIlWrhw4XVrSUpKkre3t3UJDAz8haMDAAANhd1v8LZYLDavDcOo0PZz/X/aXl5erubNm+v1119XaGioHnnkESUmJtpc6rvWzJkzVVhYaF1OnDhR3eEAAIAGxsleO27atKkcHR0rzCKdOnWqwuzRVf7+/pX2d3Jykq+vryQpICBAzs7OcnR0tPbp1KmT8vPzVVJSIhcXlwrbdXV1laur6y8dEgAAaIDsNrPk4uKi0NBQpaen27Snp6crIiKi0nXCw8Mr9E9LS1NYWJj1Zu4+ffroX//6l8rLy619vvrqKwUEBFQalAAAAMzY9TJcQkKC/vjHP2rt2rXKzs7WlClTlJubq7i4OElXLo898cQT1v5xcXE6fvy4EhISlJ2drbVr12rNmjWaOnWqtc+zzz6r06dPa/Lkyfrqq6/03nvv6aWXXtL48ePrfHwAAKD+s9tlOEmKiYnR6dOntWDBAuXl5alr165KTU1VUFCQJCkvL8/mmUvBwcFKTU3VlClTtGLFCrVo0ULLly/X8OHDrX0CAwOVlpamKVOmqFu3bmrZsqUmT56s6dOn1/n4AABA/WfX5yzdrHjOEgAA9U+De84SAABAfUBYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMEFYAgAAMFGtsHTixAl999131tf79u1TfHy8Xn/99RorDAAA4GZQrbD02GOP6aOPPpIk5efn64EHHtC+ffs0a9YsLViwoEYLBAAAsKdqhaV//OMf6tWrlyTpL3/5i7p27aq9e/fqrbfe0vr162uyPgAAALuqVlgqLS2Vq6urJGnnzp0aPHiwJKljx47Ky8urueoAAADsrFphqUuXLlq1apUyMjKUnp6uBx98UJJ08uRJ+fr61miBAAAA9lStsLRo0SL9z//8j+677z49+uij6t69uyRp+/bt1stzAAAADYHFMAyjOiuWlZWpqKhIPj4+1rZjx47Jw8NDzZs3r7EC7aGoqEje3t4qLCyUl5eXvcsBAAA3oLbO39WaWbp48aKKi4utQen48eNatmyZjh49Wu+DEgAAwE9VKywNGTJEb7zxhiTp7Nmz6t27t5YsWaKhQ4cqOTm5RgsEAACwp2qFpc8++0yRkZGSpC1btsjPz0/Hjx/XG2+8oeXLl9dogQAAAPZUrbB04cIFNW7cWJKUlpamYcOGycHBQXfffbeOHz9eowUCAADYU7XCUtu2bfXOO+/oxIkT+uCDDxQVFSVJOnXqFDdEAwCABqVaYen555/X1KlT1aZNG/Xq1Uvh4eGSrswy3XnnnTVaIAAAgD1V+9EB+fn5ysvLU/fu3eXgcCVz7du3T15eXurYsWONFlnXeHQAAAD1T22dv52qu6K/v7/8/f313XffyWKxqGXLljyQEgAANDjVugxXXl6uBQsWyNvbW0FBQWrdurWaNGmiF154QeXl5TVdIwAAgN1Ua2YpMTFRa9as0e9//3v16dNHhmFoz549mjdvni5duqSFCxfWdJ0AAAB2Ua17llq0aKFVq1Zp8ODBNu1//etfNW7cOH3//fc1VqA9cM8SAAD1z031dSdnzpyp9Cbujh076syZM7+4KAAAgJtFtcJS9+7d9dprr1Vof+2119StW7dfXBQAAMDNolr3LC1evFgPPfSQdu7cqfDwcFksFu3du1cnTpxQampqTdcIAABgN9WaWerbt6+++uor/eY3v9HZs2d15swZDRs2TP/85z+1bt26mq4RAADAbqr9UMrKHD58WD179lRZWVlNbdIuuMEbAID656a6wRsAAOBWQVgCAAAwQVgCAAAwUaVPww0bNsz0/bNnz/6SWgAAAG46VQpL3t7eP/v+E0888YsKAgAAuJlUKSzxWAAAAHCr4Z4lAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE3YPSytXrlRwcLDc3NwUGhqqjIwM0/67d+9WaGio3NzcFBISolWrVl2376ZNm2SxWDR06NAarhoAANwq7BqWUlJSFB8fr8TERGVlZSkyMlIDBgxQbm5upf1zcnI0cOBARUZGKisrS7NmzdKkSZO0devWCn2PHz+uqVOnKjIysraHAQAAGjCLYRiGvXbeu3dv9ezZU8nJyda2Tp06aejQoUpKSqrQf/r06dq+fbuys7OtbXFxcTp8+LAyMzOtbWVlZerbt6+efPJJZWRk6OzZs3rnnXduuK6ioiJ5e3ursLBQXl5e1RscAACoU7V1/rbbzFJJSYkOHjyoqKgom/aoqCjt3bu30nUyMzMr9I+OjtaBAwdUWlpqbVuwYIGaNWump59++oZqKS4uVlFRkc0CAAAg2TEsFRQUqKysTH5+fjbtfn5+ys/Pr3Sd/Pz8SvtfvnxZBQUFkqQ9e/ZozZo1Wr169Q3XkpSUJG9vb+sSGBhYxdEAAICGyu43eFssFpvXhmFUaPu5/lfbz507p8cff1yrV69W06ZNb7iGmTNnqrCw0LqcOHGiCiMAAAANmZO9dty0aVM5OjpWmEU6depUhdmjq/z9/Svt7+TkJF9fX/3zn//UsWPHNGjQIOv75eXlkiQnJycdPXpUt99+e4Xturq6ytXV9ZcOCQAANEB2m1lycXFRaGio0tPTbdrT09MVERFR6Trh4eEV+qelpSksLEzOzs7q2LGjvvjiCx06dMi6DB48WP369dOhQ4e4vAYAAKrMbjNLkpSQkKDY2FiFhYUpPDxcr7/+unJzcxUXFyfpyuWx77//Xm+88YakK598e+2115SQkKAxY8YoMzNTa9as0caNGyVJbm5u6tq1q80+mjRpIkkV2gEAAG6EXcNSTEyMTp8+rQULFigvL09du3ZVamqqgoKCJEl5eXk2z1wKDg5WamqqpkyZohUrVqhFixZavny5hg8fbq8hAACABs6uz1m6WfGcJQAA6p8G95wlAACA+oCwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYIKwBAAAYMLuYWnlypUKDg6Wm5ubQkNDlZGRYdp/9+7dCg0NlZubm0JCQrRq1Sqb91evXq3IyEj5+PjIx8dH/fv31759+2pzCAAAoAGza1hKSUlRfHy8EhMTlZWVpcjISA0YMEC5ubmV9s/JydHAgQMVGRmprKwszZo1S5MmTdLWrVutfXbt2qVHH31UH330kTIzM9W6dWtFRUXp+++/r6thAQCABsRiGIZhr5337t1bPXv2VHJysrWtU6dOGjp0qJKSkir0nz59urZv367s7GxrW1xcnA4fPqzMzMxK91FWViYfHx+99tpreuKJJ26orqKiInl7e6uwsFBeXl5VHBUAALCH2jp/221mqaSkRAcPHlRUVJRNe1RUlPbu3VvpOpmZmRX6R0dH68CBAyotLa10nQsXLqi0tFS33XbbdWspLi5WUVGRzQIAACDZMSwVFBSorKxMfn5+Nu1+fn7Kz8+vdJ38/PxK+1++fFkFBQWVrjNjxgy1bNlS/fv3v24tSUlJ8vb2ti6BgYFVHA0AAGio7H6Dt8VisXltGEaFtp/rX1m7JC1evFgbN27U22+/LTc3t+tuc+bMmSosLLQuJ06cqMoQAABAA+Zkrx03bdpUjo6OFWaRTp06VWH26Cp/f/9K+zs5OcnX19em/ZVXXtFLL72knTt3qlu3bqa1uLq6ytXVtRqjAAAADZ3dZpZcXFwUGhqq9PR0m/b09HRFRERUuk54eHiF/mlpaQoLC5Ozs7O17eWXX9YLL7ygHTt2KCwsrOaLBwAAtwy7XoZLSEjQH//4R61du1bZ2dmaMmWKcnNzFRcXJ+nK5bGffoItLi5Ox48fV0JCgrKzs7V27VqtWbNGU6dOtfZZvHixZs+erbVr16pNmzbKz89Xfn6+zp8/X+fjAwAA9Z/dLsNJUkxMjE6fPq0FCxYoLy9PXbt2VWpqqoKCgiRJeXl5Ns9cCg4OVmpqqqZMmaIVK1aoRYsWWr58uYYPH27ts3LlSpWUlOjhhx+22dfcuXM1b968OhkXAABoOOz6nKWbFc9ZAgCg/mlwz1kCAACoDwhLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJuwellauXKng4GC5ubkpNDRUGRkZpv13796t0NBQubm5KSQkRKtWrarQZ+vWrercubNcXV3VuXNnbdu2rbbKBwAADZxdw1JKSori4+OVmJiorKwsRUZGasCAAcrNza20f05OjgYOHKjIyEhlZWVp1qxZmjRpkrZu3Wrtk5mZqZiYGMXGxurw4cOKjY3ViBEj9Omnn9bVsAAAQANiMQzDsNfOe/furZ49eyo5Odna1qlTJw0dOlRJSUkV+k+fPl3bt29Xdna2tS0uLk6HDx9WZmamJCkmJkZFRUV6//33rX0efPBB+fj4aOPGjTdUV1FRkby9vVVYWCgvL6/qDg8AANSh2jp/O9XYlqqopKREBw8e1IwZM2zao6KitHfv3krXyczMVFRUlE1bdHS01qxZo9LSUjk7OyszM1NTpkyp0GfZsmXXraW4uFjFxcXW14WFhZKu/NIBAED9cPW8XdPzQHYLSwUFBSorK5Ofn59Nu5+fn/Lz8ytdJz8/v9L+ly9fVkFBgQICAq7b53rblKSkpCTNnz+/QntgYOCNDgcAANwkTp8+LW9v7xrbnt3C0lUWi8XmtWEYFdp+rv+17VXd5syZM5WQkGB9ffbsWQUFBSk3N7dGf9mouqKiIgUGBurEiRNcEr0JcDxuHhyLmwfH4uZRWFio1q1b67bbbqvR7dotLDVt2lSOjo4VZnxOnTpVYWboKn9//0r7Ozk5ydfX17TP9bYpSa6urnJ1da3Q7u3tzX/4NwkvLy+OxU2E43Hz4FjcPDgWNw8Hh5r9/JrdPg3n4uKi0NBQpaen27Snp6crIiKi0nXCw8Mr9E9LS1NYWJicnZ1N+1xvmwAAAGbsehkuISFBsbGxCgsLU3h4uF5//XXl5uYqLi5O0pXLY99//73eeOMNSVc++fbaa68pISFBY8aMUWZmptasWWPzKbfJkyfr3nvv1aJFizRkyBD99a9/1c6dO/Xxxx/bZYwAAKB+s2tYiomJ0enTp7VgwQLl5eWpa9euSk1NVVBQkCQpLy/P5plLwcHBSk1N1ZQpU7RixQq1aNFCy5cv1/Dhw619IiIitGnTJs2ePVtz5szR7bffrpSUFPXu3fuG63J1ddXcuXMrvTSHusWxuLlwPG4eHIubB8fi5lFbx8Kuz1kCAAC42dn9604AAABuZoQlAAAAE4QlAAAAE4QlAAAAE7dsWFq5cqWCg4Pl5uam0NBQZWRkmPbfvXu3QkND5ebmppCQEK1ataqOKm34qnIs3n77bT3wwANq1qyZvLy8FB4erg8++KAOq23Yqvp3cdWePXvk5OSkHj161G6Bt5iqHo/i4mIlJiYqKChIrq6uuv3227V27do6qrZhq+qx2LBhg7p37y4PDw8FBAToySef1OnTp+uo2obr73//uwYNGqQWLVrIYrHonXfe+dl1auT8bdyCNm3aZDg7OxurV682jhw5YkyePNnw9PQ0jh8/Xmn/b7/91vDw8DAmT55sHDlyxFi9erXh7OxsbNmypY4rb3iqeiwmT55sLFq0yNi3b5/x1VdfGTNnzjScnZ2Nzz77rI4rb3iqeiyuOnv2rBESEmJERUUZ3bt3r5tibwHVOR6DBw82evfubaSnpxs5OTnGp59+auzZs6cOq26YqnosMjIyDAcHB+O///u/jW+//dbIyMgwunTpYgwdOrSOK294UlNTjcTERGPr1q2GJGPbtm2m/Wvq/H1LhqVevXoZcXFxNm0dO3Y0ZsyYUWn/5557zujYsaNN23/9138Zd999d63VeKuo6rGoTOfOnY358+fXdGm3nOoei5iYGGP27NnG3LlzCUs1qKrH4/333ze8vb2N06dP10V5t5SqHouXX37ZCAkJsWlbvny50apVq1qr8VZ0I2Gpps7ft9xluJKSEh08eFBRUVE27VFRUdq7d2+l62RmZlboHx0drQMHDqi0tLTWam3oqnMsrlVeXq5z587V+Jcm3mqqeyzWrVunb775RnPnzq3tEm8p1Tke27dvV1hYmBYvXqyWLVuqffv2mjp1qi5evFgXJTdY1TkWERER+u6775SamirDMPTvf/9bW7Zs0UMPPVQXJeMnaur8bdcneNtDQUGBysrKKnyxrp+fX4Uv4L0qPz+/0v6XL19WQUGBAgICaq3ehqw6x+JaS5Ys0Y8//qgRI0bURom3jOoci6+//lozZsxQRkaGnJxuuf+V1KrqHI9vv/1WH3/8sdzc3LRt2zYVFBRo3LhxOnPmDPct/QLVORYRERHasGGDYmJidOnSJV2+fFmDBw/WH/7wh7ooGT9RU+fvW25m6SqLxWLz2jCMCm0/17+ydlRdVY/FVRs3btS8efOUkpKi5s2b11Z5t5QbPRZlZWV67LHHNH/+fLVv376uyrvlVOVvo7y8XBaLRRs2bFCvXr00cOBAvfrqq1q/fj2zSzWgKsfiyJEjmjRpkp5//nkdPHhQO3bsUE5OjvV7T1G3auL8fcv9c7Bp06ZydHSs8C+CU6dOVUifV/n7+1fa38nJSb6+vrVWa0NXnWNxVUpKip5++mlt3rxZ/fv3r80ybwlVPRbnzp3TgQMHlJWVpQkTJki6crI2DENOTk5KS0vTr371qzqpvSGqzt9GQECAWrZsKW9vb2tbp06dZBiGvvvuO7Vr165Wa26oqnMskpKS1KdPH02bNk2S1K1bN3l6eioyMlIvvvgiVyPqUE2dv2+5mSUXFxeFhoYqPT3dpj09PV0RERGVrhMeHl6hf1pamsLCwuTs7FxrtTZ01TkW0pUZpdGjR+utt97iHoAaUtVj4eXlpS+++EKHDh2yLnFxcerQoYMOHTpUpS+uRkXV+dvo06ePTp48qfPnz1vbvvrqKzk4OKhVq1a1Wm9DVp1jceHCBTk42J5eHR0dJf3frAbqRo2dv6t0O3gDcfVjoGvWrDGOHDlixMfHG56ensaxY8cMwzCMGTNmGLGxsdb+Vz96OGXKFOPIkSPGmjVreHRADanqsXjrrbcMJycnY8WKFUZeXp51OXv2rL2G0GBU9Vhci0/D1ayqHo9z584ZrVq1Mh5++GHjn//8p7F7926jXbt2xjPPPGOvITQYVT0W69atM5ycnIyVK1ca33zzjfHxxx8bYWFhRq9evew1hAbj3LlzRlZWlpGVlWVIMl599VUjKyvL+hiH2jp/35JhyTAMY8WKFUZQUJDh4uJi9OzZ09i9e7f1vVGjRhl9+/a16b9r1y7jzjvvNFxcXIw2bdoYycnJdVxxw1WVY9G3b19DUoVl1KhRdV94A1TVv4ufIizVvKoej+zsbKN///6Gu7u70apVKyMhIcG4cOFCHVfdMFX1WCxfvtzo3Lmz4e7ubgQEBBgjR440vvvuuzquuuH56KOPTM8BtXX+thgGc4IAAADXc8vdswQAAFAVhCUAAAAThCUAAAAThCUAAAAThCUAAAAThCUAAAAThCUAAAAThCUAuAEWi0XvvPOOvcsAYAeEJQA3vdGjR8tisVRYHnzwQXuXBuAW4GTvAgDgRjz44INat26dTZurq6udqgFwK2FmCUC94OrqKn9/f5vFx8dH0pVLZMnJyRowYIDc3d0VHByszZs326z/xRdf6Fe/+pXc3d3l6+ursWPH6vz58zZ91q5dqy5dusjV1VUBAQGaMGGCzfsFBQX6zW9+Iw8PD7Vr107bt2+v3UEDuCkQlgA0CHPmzNHw4cN1+PBhPf7443r00UeVnZ0tSbpw4YIefPBB+fj4aP/+/dq8ebN27txpE4aSk5M1fvx4jR07Vl988YW2b9+utm3b2uxj/vz5GjFihD7//HMNHDhQI0eO1JkzZ+p0nADs4Bd/BTAA1LJRo0YZjo6Ohqenp82yYMECwzAMQ5IRFxdns07v3r2NZ5991jAMw3j99dcNHx8f4/z589b333vvPcPBwcHIz883DMMwWrRoYSQmJl63BknG7Nmzra/Pnz9vWCwW4/3336+xcQK4OXHPEoB6oV+/fkpOTrZpu+2226w/h4eH27wXHh6uQ4cOSZKys7PVvXt3eXp6Wt/v06ePysvLdfToUVksFp08eVL333+/aQ3dunWz/uzp6anGjRvr1KlT1R0SgHqCsASgXvD09KxwWeznWCwWSZJhGNafK+vj7u5+Q9tzdnausG55eXmVagJQ/3DPEoAG4ZNPPqnwumPHjpKkzp0769ChQ/rxxx+t7+/Zs0cODg5q3769GjdurDZt2ujDDz+s05oB1A/MLAGoF4qLi5Wfn2/T5uTkpKZNm0qSNm/erLCwMN1zzz3asGGD9u3bpzVr1kiSRo4cqblz52rUqFGaN2+efvjhB02cOFGxsbHy8/OTJM2bN09xcXFq3ry5BgwYoHPnzmnPnj2aOHFi3Q4UwE2HsASgXtixY4cCAgJs2jp06KAvv/xS0pVPqm3atEnjxo2Tv7+/NmzYoM6dO0uSPDw89MEHH2jy5Mm666675OHhoeHDh+vVV1+1bmvUqFG6dOmSli5dqqlTp6pp06Z6+OGH626AAG5aFsMwDHsXAQC/hMVi0bZt2zR06FB7lwKgAeKeJQAAABOEJQAAABPcswSg3uNuAgC1iZklAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE4QlAAAAE/8Pd8ApOzoOJ8oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Abstract_1(X, y, X_cv, y_cv, test, epochs, lr):\n",
    "    # Model\n",
    "    model = nn.Sequential(\n",
    "        # Input Layer\n",
    "        nn.Linear(X.shape[1], 32),\n",
    "        nn.ReLU(),\n",
    "        # Hidden Layers\n",
    "        nn.Linear(32, 32),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(32, 16),\n",
    "        nn.ReLU(),\n",
    "        # Output Layer\n",
    "        nn.Linear(16, 1),\n",
    "    )\n",
    "    # Loss and Optimizer with L2 Regularization\n",
    "    criterion = nn.L1Loss()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=0.004)\n",
    "\n",
    "    # Train the Model\n",
    "    train_loss = []\n",
    "    cv_loss = []\n",
    "    for epoch in range(epochs):\n",
    "\n",
    "        # Forward pass\n",
    "        outputs = model(X.float())\n",
    "        outputs_cv = model(X_cv.float())\n",
    "        loss = criterion(outputs, y.float())\n",
    "        loss_cv = criterion(outputs_cv, y_cv.float())\n",
    "\n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Store the loss values\n",
    "        train_loss.append(loss.item())\n",
    "        cv_loss.append(loss_cv.item())\n",
    "\n",
    "        # Print Loss and CV Loss\n",
    "        if (epoch+1) % 100 == 0:\n",
    "            print ('Epoch [%d/%d], Train Loss: %.4f, CV Loss: %.4f' \n",
    "                   %(epoch+1, epochs, loss.item(), loss_cv.item()))\n",
    "\n",
    "    # Plot the loss curve\n",
    "    plt.plot(train_loss, label='Training Loss')\n",
    "    plt.plot(cv_loss, label='Cross-Validation Loss')\n",
    "    plt.legend()\n",
    "    plt.xlim(0, epochs)\n",
    "    plt.ylim(0, 0.1)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.show()\n",
    "\n",
    "    # Save the Model\n",
    "    torch.save(model.state_dict(), 'model_ab3.pkl')\n",
    "\n",
    "# Call the function\n",
    "Abstract_1(X_t, y_t, X_cv, y_cv, test, 1, 0.00001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pred_ab1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/z4/nf90t5tj0zs4b4mhbdkmmy200000gn/T/ipykernel_2068/2436336252.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_ab1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pred_ab1' is not defined"
     ]
    }
   ],
   "source": [
    "print(pred_ab1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mRunning cells with 'base' requires the ipykernel package.\n",
      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
      "\u001b[1;31mCommand: 'conda install -n base ipykernel --update-deps --force-reinstall'"
     ]
    }
   ],
   "source": [
    "# convert from 1logp format to normal p format\n",
    "pred_ab1 = np.exp(pred_ab1.detach().numpy()) - 1\n",
    "print(pred_ab1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mRunning cells with 'base' requires the ipykernel package.\n",
      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
      "\u001b[1;31mCommand: 'conda install -n base ipykernel --update-deps --force-reinstall'"
     ]
    }
   ],
   "source": [
    "#Create csv file for submission\n",
    "sub_df = pd.read_csv('sample_submission.csv', index_col = \"Id\")\n",
    "sub_df[\"SalePrice\"] = pred_ab1\n",
    "sub_df.to_csv('submission_ab1_5.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mRunning cells with 'base' requires the ipykernel package.\n",
      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
      "\u001b[1;31mCommand: 'conda install -n base ipykernel --update-deps --force-reinstall'"
     ]
    }
   ],
   "source": []
  }
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