main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Linear Regression using the PyTorch library\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.26.4\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "print(np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = np.loadtxt(\"datasets/Salary_dataset.csv\", delimiter=\",\", skiprows=1)\n",
    "X = dataset[:, 1]  # Years of Experience\n",
    "Y = dataset[:, 2]  # Salary\n",
    "\n",
    "N = len(X)\n",
    "\n",
    "# Convert arrays to the correct shape\n",
    "X = X.reshape(N, 1)\n",
    "Y = Y.reshape(N, 1)\n",
    "\n",
    "scaler_X = StandardScaler()\n",
    "scaler_Y = StandardScaler()\n",
    "\n",
    "X_scaled = scaler_X.fit_transform(X)\n",
    "Y_scaled = scaler_Y.fit_transform(Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x123ef0e10>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = torch.from_numpy(X_scaled.astype(np.float32))\n",
    "targets = torch.from_numpy(Y_scaled.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nn.Linear(1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "optimiser = torch.optim.SGD(model.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/100, Loss: 0.0430\n",
      "Epoch: 2/100, Loss: 0.0430\n",
      "Epoch: 3/100, Loss: 0.0430\n",
      "Epoch: 4/100, Loss: 0.0430\n",
      "Epoch: 5/100, Loss: 0.0430\n",
      "Epoch: 6/100, Loss: 0.0430\n",
      "Epoch: 7/100, Loss: 0.0430\n",
      "Epoch: 8/100, Loss: 0.0430\n",
      "Epoch: 9/100, Loss: 0.0430\n",
      "Epoch: 10/100, Loss: 0.0430\n",
      "Epoch: 11/100, Loss: 0.0430\n",
      "Epoch: 12/100, Loss: 0.0430\n",
      "Epoch: 13/100, Loss: 0.0430\n",
      "Epoch: 14/100, Loss: 0.0430\n",
      "Epoch: 15/100, Loss: 0.0430\n",
      "Epoch: 16/100, Loss: 0.0430\n",
      "Epoch: 17/100, Loss: 0.0430\n",
      "Epoch: 18/100, Loss: 0.0430\n",
      "Epoch: 19/100, Loss: 0.0430\n",
      "Epoch: 20/100, Loss: 0.0430\n",
      "Epoch: 21/100, Loss: 0.0430\n",
      "Epoch: 22/100, Loss: 0.0430\n",
      "Epoch: 23/100, Loss: 0.0430\n",
      "Epoch: 24/100, Loss: 0.0430\n",
      "Epoch: 25/100, Loss: 0.0430\n",
      "Epoch: 26/100, Loss: 0.0430\n",
      "Epoch: 27/100, Loss: 0.0430\n",
      "Epoch: 28/100, Loss: 0.0430\n",
      "Epoch: 29/100, Loss: 0.0430\n",
      "Epoch: 30/100, Loss: 0.0430\n",
      "Epoch: 31/100, Loss: 0.0430\n",
      "Epoch: 32/100, Loss: 0.0430\n",
      "Epoch: 33/100, Loss: 0.0430\n",
      "Epoch: 34/100, Loss: 0.0430\n",
      "Epoch: 35/100, Loss: 0.0430\n",
      "Epoch: 36/100, Loss: 0.0430\n",
      "Epoch: 37/100, Loss: 0.0430\n",
      "Epoch: 38/100, Loss: 0.0430\n",
      "Epoch: 39/100, Loss: 0.0430\n",
      "Epoch: 40/100, Loss: 0.0430\n",
      "Epoch: 41/100, Loss: 0.0430\n",
      "Epoch: 42/100, Loss: 0.0430\n",
      "Epoch: 43/100, Loss: 0.0430\n",
      "Epoch: 44/100, Loss: 0.0430\n",
      "Epoch: 45/100, Loss: 0.0430\n",
      "Epoch: 46/100, Loss: 0.0430\n",
      "Epoch: 47/100, Loss: 0.0430\n",
      "Epoch: 48/100, Loss: 0.0430\n",
      "Epoch: 49/100, Loss: 0.0430\n",
      "Epoch: 50/100, Loss: 0.0430\n",
      "Epoch: 51/100, Loss: 0.0430\n",
      "Epoch: 52/100, Loss: 0.0430\n",
      "Epoch: 53/100, Loss: 0.0430\n",
      "Epoch: 54/100, Loss: 0.0430\n",
      "Epoch: 55/100, Loss: 0.0430\n",
      "Epoch: 56/100, Loss: 0.0430\n",
      "Epoch: 57/100, Loss: 0.0430\n",
      "Epoch: 58/100, Loss: 0.0430\n",
      "Epoch: 59/100, Loss: 0.0430\n",
      "Epoch: 60/100, Loss: 0.0430\n",
      "Epoch: 61/100, Loss: 0.0430\n",
      "Epoch: 62/100, Loss: 0.0430\n",
      "Epoch: 63/100, Loss: 0.0430\n",
      "Epoch: 64/100, Loss: 0.0430\n",
      "Epoch: 65/100, Loss: 0.0430\n",
      "Epoch: 66/100, Loss: 0.0430\n",
      "Epoch: 67/100, Loss: 0.0430\n",
      "Epoch: 68/100, Loss: 0.0430\n",
      "Epoch: 69/100, Loss: 0.0430\n",
      "Epoch: 70/100, Loss: 0.0430\n",
      "Epoch: 71/100, Loss: 0.0430\n",
      "Epoch: 72/100, Loss: 0.0430\n",
      "Epoch: 73/100, Loss: 0.0430\n",
      "Epoch: 74/100, Loss: 0.0430\n",
      "Epoch: 75/100, Loss: 0.0430\n",
      "Epoch: 76/100, Loss: 0.0430\n",
      "Epoch: 77/100, Loss: 0.0430\n",
      "Epoch: 78/100, Loss: 0.0430\n",
      "Epoch: 79/100, Loss: 0.0430\n",
      "Epoch: 80/100, Loss: 0.0430\n",
      "Epoch: 81/100, Loss: 0.0430\n",
      "Epoch: 82/100, Loss: 0.0430\n",
      "Epoch: 83/100, Loss: 0.0430\n",
      "Epoch: 84/100, Loss: 0.0430\n",
      "Epoch: 85/100, Loss: 0.0430\n",
      "Epoch: 86/100, Loss: 0.0430\n",
      "Epoch: 87/100, Loss: 0.0430\n",
      "Epoch: 88/100, Loss: 0.0430\n",
      "Epoch: 89/100, Loss: 0.0430\n",
      "Epoch: 90/100, Loss: 0.0430\n",
      "Epoch: 91/100, Loss: 0.0430\n",
      "Epoch: 92/100, Loss: 0.0430\n",
      "Epoch: 93/100, Loss: 0.0430\n",
      "Epoch: 94/100, Loss: 0.0430\n",
      "Epoch: 95/100, Loss: 0.0430\n",
      "Epoch: 96/100, Loss: 0.0430\n",
      "Epoch: 97/100, Loss: 0.0430\n",
      "Epoch: 98/100, Loss: 0.0430\n",
      "Epoch: 99/100, Loss: 0.0430\n",
      "Epoch: 100/100, Loss: 0.0430\n"
     ]
    }
   ],
   "source": [
    "losses = []\n",
    "\n",
    "# Gradient Descent loop\n",
    "n_epochs = 100\n",
    "for it in range(n_epochs):\n",
    "    # avoid aggregating gradients\n",
    "    optimiser.zero_grad()\n",
    "\n",
    "    # forward pass\n",
    "    outputs = model(inputs)\n",
    "\n",
    "    # calculate loss\n",
    "    loss = criterion(outputs, targets)\n",
    "\n",
    "    losses.append(loss.item())\n",
    "\n",
    "    loss.backward()\n",
    "    optimiser.step()\n",
    "\n",
    "    print(f\"Epoch: {it+1}/{n_epochs}, Loss: {losses[-1]:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x123fd5f50>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = model(inputs).detach().numpy()\n",
    "plt.scatter(X, Y, label=\"Original Data\")\n",
    "predicted = scaler_Y.inverse_transform(predicted)\n",
    "plt.plot(X, predicted, label=\"Fitted Line\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.9782415]] [1.4202488e-08]\n"
     ]
    }
   ],
   "source": [
    "w = model.weight.data.numpy()\n",
    "b = model.bias.data.numpy()\n",
    "\n",
    "print(w, b)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch-linear-regression-EgWqk-lY",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}