salary_data_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "8f39e46e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from tqdm import trange\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "9094033d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(   YearsExperience    Salary\n",
       " 0              7.9  101302.0\n",
       " 1              4.0   55794.0\n",
       " 2              3.7   57189.0\n",
       " 3              3.2   64445.0\n",
       " 4              9.6  112635.0,\n",
       " (30, 2))"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./datasets/Salary_Data.csv\")\n",
    "dataset = dataset.sample(frac=1).reset_index(drop=True)\n",
    "dataset.head(), dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "525ee279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 7.9,  4. ,  3.7,  3.2,  9.6,  7.1,  6.8,  1.5,  3.2,  4.1,  9. ,\n",
       "         5.1, 10.3,  9.5,  4.9,  8.2,  4.5,  1.3,  4. ,  5.9,  6. ,  5.3,\n",
       "         2.9,  8.7,  1.1, 10.5,  2. ,  2.2,  3.9,  3. ]),\n",
       " array([101302.,  55794.,  57189.,  64445., 112635.,  98273.,  91738.,\n",
       "         37731.,  54445.,  57081., 105582.,  66029., 122391., 116969.,\n",
       "         67938., 113812.,  61111.,  46205.,  56957.,  81363.,  93940.,\n",
       "         83088.,  56642., 109431.,  39343., 121872.,  43525.,  39891.,\n",
       "         63218.,  60150.]))"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.to_numpy()\n",
    "X, y = dataset[:,0], dataset[:,1]\n",
    "X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "bd474ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fcfe77c1c70>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "1e7ad1e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20,) (10,) (20,) (10,)\n",
      "(20, 1) (10, 1) (20, 1) (10, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "X_train = X_train.reshape(-1, 1)\n",
    "X_test = X_test.reshape(-1, 1)\n",
    "y_train = y_train.reshape(-1, 1)\n",
    "y_test = y_test.reshape(-1, 1)\n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "02c8b05e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = torch.from_numpy(X_train.astype(np.float32))\n",
    "X_test = torch.from_numpy(X_test.astype(np.float32))\n",
    "y_train = torch.from_numpy(y_train.astype(np.float32))\n",
    "y_test = torch.from_numpy(y_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "9b3eb601",
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.01\n",
    "n_iters = 5000\n",
    "\n",
    "input_size, output_size = 1, 1\n",
    "\n",
    "model = nn.Linear(input_size, output_size)\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "512b030e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loss: 31924298.00000:  57%|█████████████████████████████████                         | 2852/5000 [00:02<00:01, 1297.93it/s]IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "loss: 31924298.00000: 100%|██████████████████████████████████████████████████████████| 5000/5000 [00:03<00:00, 1358.95it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcfe7d15d00>]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = []\n",
    "for epoch in (t := trange(n_iters)):\n",
    "    \n",
    "    y_predicted = model(X_train)\n",
    "    \n",
    "    loss = criterion(y_predicted, y_train)\n",
    "    \n",
    "    \n",
    "    loss.backward()\n",
    "    \n",
    "    \n",
    "    optimizer.step()\n",
    "    \n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    \n",
    "    losses.append(loss.item())\n",
    "    \n",
    "    t.set_description(\"loss: %.5f\" % loss.item())\n",
    "    \n",
    "plt.plot(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "03eb8127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fcfe7f84940>"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = model(X_test).detach().numpy()\n",
    "plt.scatter(X_test, predicted)\n",
    "plt.scatter(X_test, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}