Machine_Learning/Gradient_descent/gradientdescent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data into datframe\n",
    "df = pd.read_csv(\"./homeprices_banglore.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2770</td>\n",
       "      <td>3</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1200</td>\n",
       "      <td>2</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3300</td>\n",
       "      <td>4</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1800</td>\n",
       "      <td>3</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3700</td>\n",
       "      <td>5</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area  bedrooms  price\n",
       "18  2770         3  102.0\n",
       "4   1200         2   51.0\n",
       "7   3300         4  155.0\n",
       "10  1800         3   82.0\n",
       "9   3700         5  167.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using min-max Scaling\n",
    "from sklearn import preprocessing\n",
    "sx = preprocessing.MinMaxScaler()\n",
    "sy = preprocessing.MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "independent_df = df.drop('price', axis = 'columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08827586, 0.25      ],\n",
       "       [0.62068966, 0.75      ],\n",
       "       [0.22068966, 0.5       ],\n",
       "       [0.24862069, 0.5       ],\n",
       "       [0.13793103, 0.25      ],\n",
       "       [0.12758621, 0.25      ],\n",
       "       [0.6662069 , 0.75      ],\n",
       "       [0.86206897, 0.75      ],\n",
       "       [0.17586207, 0.5       ],\n",
       "       [1.        , 1.        ],\n",
       "       [0.34482759, 0.5       ],\n",
       "       [0.68448276, 0.75      ],\n",
       "       [0.06896552, 0.25      ],\n",
       "       [0.10344828, 0.25      ],\n",
       "       [0.5       , 0.5       ],\n",
       "       [0.12931034, 0.25      ],\n",
       "       [0.13103448, 0.5       ],\n",
       "       [0.25517241, 0.5       ],\n",
       "       [0.67931034, 0.5       ],\n",
       "       [0.        , 0.        ]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_X = sx.fit_transform(independent_df)\n",
    "scaled_X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependent_df = df[['price']].values.reshape(df.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 39.07],\n",
       "       [120.  ],\n",
       "       [ 62.  ],\n",
       "       [ 75.  ],\n",
       "       [ 51.  ],\n",
       "       [ 38.  ],\n",
       "       [135.  ],\n",
       "       [155.  ],\n",
       "       [ 50.  ],\n",
       "       [167.  ],\n",
       "       [ 82.  ],\n",
       "       [140.  ],\n",
       "       [ 38.  ],\n",
       "       [ 40.  ],\n",
       "       [101.  ],\n",
       "       [ 42.  ],\n",
       "       [ 48.  ],\n",
       "       [ 60.  ],\n",
       "       [102.  ],\n",
       "       [ 32.  ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dependent_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05237037],\n",
       "       [0.65185185],\n",
       "       [0.22222222],\n",
       "       [0.31851852],\n",
       "       [0.14074074],\n",
       "       [0.04444444],\n",
       "       [0.76296296],\n",
       "       [0.91111111],\n",
       "       [0.13333333],\n",
       "       [1.        ],\n",
       "       [0.37037037],\n",
       "       [0.8       ],\n",
       "       [0.04444444],\n",
       "       [0.05925926],\n",
       "       [0.51111111],\n",
       "       [0.07407407],\n",
       "       [0.11851852],\n",
       "       [0.20740741],\n",
       "       [0.51851852],\n",
       "       [0.        ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_Y = sy.fit_transform(dependent_df)\n",
    "scaled_Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BATCH GRADIENT DESCENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.70712464, 0.67456527]), -0.23034857438407427, 0.0068641890429808105)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def batch_gradient_descent(X, y_true, epochs, learning_rate = 0.01):\n",
    "  number_of_features = X.shape[1]\n",
    "  w = np.ones(shape = (number_of_features))\n",
    "  b = 0\n",
    "  total_samples = X.shape[0]\n",
    "  cost_list = []\n",
    "  epoch_list =[]\n",
    "  for i in range(epochs):\n",
    "    y_pred = np.dot(w, scaled_X.T)+ b ##w1 * area + w2 * bedrooms\n",
    "\n",
    "    w_grad = -(2 / total_samples) * (X.T.dot(y_true - y_pred))\n",
    "    b_grad = -(2 / total_samples) * np.sum(y_true - y_pred)\n",
    "\n",
    "    w = w - learning_rate * w_grad\n",
    "    b = b - learning_rate * b_grad\n",
    "\n",
    "    cost = np.mean(np.square(y_true - y_pred))\n",
    "\n",
    "    if i % 10 == 0:\n",
    "      cost_list.append(cost)\n",
    "      epoch_list.append(i)\n",
    "  return w, b, cost, cost_list, epoch_list\n",
    "w, b, cost, cost_list, epoch_list = batch_gradient_descent(scaled_X, scaled_Y.reshape(scaled_Y.shape[0],), 500)\n",
    "w, b, cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2dc36c84d10>]"
      ]
     },
     "execution_count": 13,
     "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.xlabel('epoch')\n",
    "plt.ylabel('cost')\n",
    "plt.plot(epoch_list, cost_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "STOICHASTIC GRADIENT DESCENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.70983344, 0.67557544]), -0.23086086184836468, 0.0065214615781462185)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def stoichastic_gradient_descent(X, y_true, epochs, learning_rate = 0.01):\n",
    "  number_of_features = X.shape[1]\n",
    "  w = np.ones(shape = (number_of_features))\n",
    "  b = 0\n",
    "  total_samples = X.shape[0]\n",
    "  cost_list = []\n",
    "  epoch_list = []\n",
    "  for i in range(epochs):\n",
    "    random_index = random.randint(0, total_samples - 1)\n",
    "    sample_x = X[random_index]\n",
    "    sample_y = y_true[random_index]\n",
    "    y_pred = np.dot(w, sample_x.T) + b\n",
    "    w_grad = -(2 / total_samples) * (sample_x.T.dot(sample_y - y_pred))\n",
    "    b_grad = -(2 / total_samples) * (sample_y - y_pred)\n",
    "    w = w - learning_rate * w_grad\n",
    "    b = b - learning_rate * b_grad\n",
    "\n",
    "    cost = np.square(sample_y - y_pred)\n",
    "    if i % 100 == 0:\n",
    "      cost_list.append(cost)\n",
    "      epoch_list.append(i)\n",
    "  return w, b, cost,cost_list, epoch_list\n",
    "w_sgd, b_sgd,cost_sgd,cost_list_sgd, epoch_list_sgd = stoichastic_gradient_descent(scaled_X, scaled_Y.reshape(scaled_Y.shape[0],), 10000)\n",
    "w_sgd,b_sgd,cost_sgd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2dc38e96690>]"
      ]
     },
     "execution_count": 15,
     "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.xlabel('epoch')\n",
    "plt.ylabel('cost')\n",
    "plt.plot(epoch_list_sgd, cost_list_sgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MINI BATCH GRADIENT DESCENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.70997314, 0.67800373]), -0.23359918477033226, 0.003372373031870135)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mini_batch_gradient_descent(X, y_true, epochs = 100, batch_size = 5, learning_rate = 0.01):\n",
    "  number_of_features = X.shape[1]\n",
    "  w = np.ones(shape = (number_of_features))\n",
    "  b = 0\n",
    "  total_samples = X.shape[0]\n",
    "  if batch_size > total_samples:\n",
    "    batch_size = total_samples\n",
    "  cost_list = []\n",
    "  epoch_list = []\n",
    "\n",
    "  num_batches = int(total_samples / batch_size)\n",
    "\n",
    "  for i in range(epochs):\n",
    "    random_indices = np.random.permutation(total_samples)\n",
    "    X_tmp = X[random_indices]\n",
    "    y_tmp = y_true[random_indices]\n",
    "\n",
    "    for j in range(0, total_samples, batch_size):\n",
    "      Xj = X_tmp[j : j + batch_size]\n",
    "      yj = y_tmp[j : j + batch_size]\n",
    "\n",
    "      y_pred = np.dot(w, Xj.T) + b\n",
    "\n",
    "      w_grad = -(2 / len(Xj)) * (Xj.T.dot(yj - y_pred))\n",
    "      b_grad = -(2 / len(Xj)) * np.sum(yj - y_pred)\n",
    "\n",
    "      w = w - learning_rate * w_grad\n",
    "      b = b - learning_rate * b_grad\n",
    "\n",
    "      cost = np.mean(np.square(yj - y_pred))\n",
    "    if i % 10 == 0:\n",
    "      cost_list.append(cost)\n",
    "      epoch_list.append(i)\n",
    "\n",
    "  return w, b, cost, cost_list, epoch_list\n",
    "w, b, cost, cost_list, epoch_list = mini_batch_gradient_descent(\n",
    "    scaled_X,\n",
    "    scaled_Y.reshape(scaled_Y.shape[0],),\n",
    "    epochs = 120,\n",
    "    batch_size = 5\n",
    ")\n",
    "w, b, cost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2dc36cfd610>]"
      ]
     },
     "execution_count": 17,
     "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(epoch_list, cost_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}