{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7841538a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a5125b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/multipleFeatures.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "371a40f1",
   "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>bathrooms</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2100</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1800</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1900</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3000</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2200</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  bedrooms  bathrooms  age   price\n",
       "0  2100         3          2    5  320000\n",
       "1  1800         2          1   10  250000\n",
       "2  2400         4          3    8  450000\n",
       "3  1900         3          2    6  300000\n",
       "4  3000         4          3    2  500000\n",
       "5  2200         3          2    4  350000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74bab28e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "i = df.iloc[0]\n",
    "i.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "422236c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x: Features: area, bedrooms, bathrooms, age\n",
    "#y: Target : Price\n",
    "x = []\n",
    "y = []\n",
    "for i in range(df.shape[0]):\n",
    "    temp = df.iloc[i]\n",
    "    x.append(temp[0:-1])\n",
    "    y.append(temp[-1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4859cb55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2100,    3,    2,    5],\n",
       "       [1800,    2,    1,   10],\n",
       "       [2400,    4,    3,    8],\n",
       "       [1900,    3,    2,    6],\n",
       "       [3000,    4,    3,    2],\n",
       "       [2200,    3,    2,    4]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_values = np.array(x)\n",
    "\n",
    "x_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb85de2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([320000, 250000, 450000, 300000, 500000, 350000])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_values = np.array(y)\n",
    "\n",
    "y_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c9770ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    " def calculate_cost(x_values, y_values, w_values, b): \n",
    "    \n",
    "    m = x_values.shape[0]\n",
    "    cost = 0\n",
    "    for i in range(m):                                \n",
    "        f_wb_i = np.dot(x_values[i], w_values) + b           \n",
    "        cost = cost + (f_wb_i - y_values[i])**2       \n",
    "    return cost / (2 * m)      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d9e9681",
   "metadata": {},
   "source": [
    "Gradient descent - multiple features:\n",
    "\n",
    "$$\\begin{align*} \\text{repeat}&\\text{ until convergence:} \\; \\lbrace \\newline\\;\n",
    "& w_j = w_j -  \\alpha \\frac{\\partial J(\\mathbf{w},b)}{\\partial w_j} \\tag{5}  \\; & \\text{for j = 0..n-1}\\newline\n",
    "&b\\ \\ = b -  \\alpha \\frac{\\partial J(\\mathbf{w},b)}{\\partial b}  \\newline \\rbrace\n",
    "\\end{align*}$$\n",
    "\n",
    "n: features \n",
    "\n",
    "-*simulatenous update*\n",
    "$$\n",
    "\\begin{align}\n",
    "\\frac{\\partial J(\\mathbf{w},b)}{\\partial w_j}  &= \\frac{1}{m} \\sum\\limits_{i = 0}^{m-1} (f_{\\mathbf{w},b}(\\mathbf{x}^{(i)}) - y^{(i)})x_{j}^{(i)} \\tag{6}  \\\\\n",
    "\\frac{\\partial J(\\mathbf{w},b)}{\\partial b}  &= \\frac{1}{m} \\sum\\limits_{i = 0}^{m-1} (f_{\\mathbf{w},b}(\\mathbf{x}^{(i)}) - y^{(i)}) \\tag{7}\n",
    "\\end{align}\n",
    "$$\n",
    "* m : training examples\n",
    "    \n",
    "*  $f_{\\mathbf{w},b}(\\mathbf{x}^{(i)})$: prediction, $y^{(i)}$: target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f01c07ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_gradient_descent(x_values, y_values, w_values, b): \n",
    "    m,n = x_values.shape     \n",
    "    #m : examples ; n: features \n",
    "    derivatives_w = np.zeros((n,))\n",
    "    derivative_b = 0\n",
    "\n",
    "    for i in range(m):                             \n",
    "        error = (np.dot(x_values[i], w_values) + b) - y_values[i]   \n",
    "        for j in range(n):                         \n",
    "            derivatives_w[j] = derivatives_w[j] + error * x_values[i, j]    \n",
    "        derivative_b = derivative_b + error                        \n",
    "                               \n",
    "        \n",
    "    return derivative_b/m, derivatives_w/m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "edd2797c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "\n",
    "def gradient_descent(x_values, y_values, w_initial_values, b_initial, cost_function, gradient_function, alpha, iterations): \n",
    "    \n",
    "\n",
    "    cost_cache = []\n",
    "    w_values = copy.deepcopy(w_initial_values)  \n",
    "    b = b_initial\n",
    "    for i in range(iterations):\n",
    "\n",
    "        derivative_b,derivatives_w = gradient_function(x_values, y_values, w_values, b)   \n",
    "\n",
    "        w_values = w_values - alpha * derivatives_w              \n",
    "        b = b - alpha * derivative_b\n",
    "        \n",
    "        if i<100000:\n",
    "            cost_cache.append(cost_function(x_values, y_values, w_values, b))\n",
    "            \n",
    "    return w_values, b, cost_cache "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "04db3611",
   "metadata": {},
   "outputs": [],
   "source": [
    "w_initial_values = np.zeros(x_values.shape[1])\n",
    "b_initial = 0\n",
    "\n",
    "iterations = 500000\n",
    "learning_rate = 3.0e-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82a904c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_normalized = (x - np.mean(x, axis=0)) / np.std(x, axis=0)\n",
    "\n",
    "y_normalized = (y - np.mean(y)) / np.std(y)\n",
    "\n",
    "w_final_values, b_final, cost_cache = gradient_descent(x_normalized, y_normalized, w_initial_values,b_initial ,\n",
    "                                                    calculate_cost, calculate_gradient_descent, \n",
    "                                                    learning_rate, iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "158d420f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(cost_cache)\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Cost')\n",
    "plt.title('Cost vs. Iteration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7406af80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[438515.69643401 237921.36543737]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(x_values, y_values)\n",
    "\n",
    "new_data = pd.DataFrame({'area': [3000, 2000], 'bedrooms': [3, 2], 'bathrooms': [2, 1], 'age': [1, 2]})\n",
    "new_data_values = new_data.values\n",
    "predictions = model.predict(new_data_values)\n",
    "\n",
    "print(predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5ce2cf9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([433937.14711791, 237831.75441513])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_normalized = (new_data_values - np.mean(x_values, axis=0)) / np.std(x_values, axis=0)\n",
    "\n",
    "prediction_normalized = np.dot(new_data_normalized, w_final_values) + b_final\n",
    "\n",
    "predictions_custom = prediction_normalized * np.std(y_values) + np.mean(y_values)\n",
    "\n",
    "predictions_custom\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fbff08a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming that x_train, y_train and z_train are your data\n",
    "#2 features; 1 target \n",
    "\n",
    "x_train = np.array(df[\"area\"])\n",
    "y_train = np.array(df[\"age\"])\n",
    "z_train = np.array(df[\"price\"])\n",
    "x = x_train.reshape(-1, 1)\n",
    "y = y_train.reshape(-1, 1)\n",
    "z = z_train.reshape(-1, 1)\n",
    "\n",
    "# For multiple variables, we need to concatenate them first\n",
    "xy = np.concatenate((x, y), axis=1)\n",
    "# Fit the Linear Regression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(xy, z)\n",
    "\n",
    "# Generate the prediction\n",
    "z_pred = regressor.predict(xy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5c8dc475",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming that x_train, y_train and z_train, and w_train are your data\n",
    "#4 features; 1 target \n",
    "\n",
    "x_train = np.array(df[\"area\"])\n",
    "y_train = np.array(df[\"bedrooms\"])\n",
    "z_train = np.array(df[\"bathrooms\"])\n",
    "w_train = np.array(df[\"age\"])\n",
    "k_train = np.array(df[\"price\"])\n",
    "\n",
    "x = x_train.reshape(-1, 1)\n",
    "y = y_train.reshape(-1, 1)\n",
    "z = z_train.reshape(-1, 1)\n",
    "w = w_train.reshape(-1, 1)\n",
    "k = k_train.reshape(-1, 1)\n",
    "\n",
    "\n",
    "# For multiple variables, we need to concatenate them first\n",
    "xyzw = np.concatenate((x, y, z, w), axis=1)\n",
    "\n",
    "# Fit the Linear Regression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(xyzw, k)\n",
    "\n",
    "# Generate the prediction\n",
    "k_pred = regressor.predict(xyzw)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ae3c7130",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Data (y):\n",
      " [320000 250000 450000 300000 500000 350000] \n",
      "\n",
      "- Sci-Kit Learn -\n",
      "Predictions(raw):\n",
      " [327881.74337092 250484.60835111 447071.01493447 303876.8668089\n",
      " 503413.59341664 337272.17311795]\n",
      "Predictions(rounded):\n",
      " [330000. 250000. 450000. 300000. 500000. 340000.]\n",
      "\n",
      "\n",
      "- Custom Model -\n",
      "Predictions(raw):\n",
      " [328823.97404592 248294.83106031 447402.37152535 305993.953009\n",
      " 501623.96420091 337860.90615851]\n",
      "Predictions(rounded):\n",
      " [330000. 250000. 450000. 310000. 500000. 340000.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Original Data (y):\\n\", k_train, \"\\n\")\n",
    "\n",
    "print(\"- Sci-Kit Learn -\")\n",
    "predictions_lib = model.predict(x_values)\n",
    "print(\"Predictions(raw):\\n\", predictions_lib)\n",
    "rounded_predictions_lib = np.round(predictions_lib / 10000) * 10000\n",
    "print(\"Predictions(rounded):\\n\", rounded_predictions_lib)\n",
    "\n",
    "pred_normalized = np.dot(np.array(x_normalized), w_final_values) + b_final\n",
    "\n",
    "pred = pred_normalized * np.std(y_values) + np.mean(y_values)\n",
    "print(\"\\n\\n- Custom Model -\")\n",
    "print(\"Predictions(raw):\\n\", pred)\n",
    "rounded_predictions = np.round(pred / 10000) * 10000\n",
    "print(\"Predictions(rounded):\\n\", rounded_predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "027eb4ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CUSTOM MODEL GRAPH\n",
    "# creating subplots (ORIGINAL - box, RAW - *, ROUNDED - .) \n",
    "fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n",
    "\n",
    "fig.suptitle('Custom Model', fontsize=16)\n",
    "\n",
    "#plotting for area feature\n",
    "axs[0, 0].scatter(x_train, k_train, marker='o', color='red', label='Original data')\n",
    "axs[0, 0].scatter(x_train, pred, marker='+', color='black', label='Predicted(raw)')\n",
    "axs[0, 0].scatter(x_train, rounded_predictions, marker='x', color='green', label='Predicted(rounded)')\n",
    "axs[0, 0].set_xlabel('Area')\n",
    "axs[0, 0].set_ylabel('Price')\n",
    "axs[0, 0].legend()\n",
    "\n",
    "#plotting for bedrooms feature\n",
    "#note (y_train is not targets) -> y_train = np.array(df[\"bedrooms\"]); k_train is the target -> k_train = np.array(df[\"price\"])\n",
    "axs[0, 1].scatter(y_train, k_train, marker='o', color='red', label='Original data')\n",
    "axs[0, 1].scatter(y_train, pred, marker='+', color='black', label='Predicted(raw)')\n",
    "axs[0, 1].scatter(y_train, rounded_predictions, marker='x', color='green', label='Predicted(rounded)')\n",
    "axs[0, 1].set_xlabel('Bedrooms')\n",
    "axs[0, 1].set_ylabel('Price')\n",
    "axs[0, 1].legend()\n",
    "\n",
    "#plotting for bathrooms feature\n",
    "axs[1, 0].scatter(z_train, k_train, marker='o', color='red', label='Original data')\n",
    "axs[1, 0].scatter(z_train, pred, marker='+', color='black', label='Predicted(raw)')\n",
    "axs[1, 0].scatter(z_train, rounded_predictions, marker='x', color='green', label='Predicted(rounded)')\n",
    "axs[1, 0].set_xlabel('Bathrooms')\n",
    "axs[1, 0].set_ylabel('Price')\n",
    "axs[1, 0].legend()\n",
    "\n",
    "#plotting for age feature\n",
    "axs[1, 1].scatter(w_train, k_train, marker='o', color='red', label='Original data')\n",
    "axs[1, 1].scatter(w_train, pred, marker='+', color='black', label='Predicted(raw)')\n",
    "axs[1, 1].scatter(w_train, rounded_predictions, marker='x', color='green', label='Predicted(rounded)')\n",
    "axs[1, 1].set_xlabel('Age')\n",
    "axs[1, 1].set_ylabel('Price')\n",
    "axs[1, 1].legend()\n",
    "\n",
    "# displaying the figure with subplots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "124f27d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SCI-KIT LEARN GRAPH\n",
    "# creating subplots (ORIGINAL - box, RAW - *, ROUNDED - .) \n",
    "fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n",
    "# Set a title for the entire figure\n",
    "fig.suptitle('SciKit Learn', fontsize=16)\n",
    "features = ['Area', 'Bedrooms', 'Bathrooms', 'Age']\n",
    "\n",
    "# looping over each subplot to plot data\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        index = 2*i + j  # calculating the index\n",
    "        axs[i, j].scatter(x_values[:,index], k_train, marker='o', color='red', label='Original data')\n",
    "        axs[i, j].scatter(x_values[:,index], predictions_lib, marker='+', color='black', label='Predicted(raw)')\n",
    "        axs[i, j].scatter(x_values[:,index], rounded_predictions_lib, marker='x', color='green', label='Predicted(rounded)')\n",
    "        axs[i, j].set_xlabel(features[index])\n",
    "        axs[i, j].set_ylabel('Price')\n",
    "        axs[i, j].legend()\n",
    "\n",
    "# displaying the figure with subplots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0bc8b4a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Custom Model Vs SciKit Learn (ROUNDED)\n",
    "# creating subplots (ORIGINAL - box, RAW - *, ROUNDED - .) \n",
    "fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n",
    "# Set a title for the entire figure\n",
    "fig.suptitle('Custom Model Vs SciKit Learn (ROUNDED)', fontsize=16)\n",
    "features = ['Area', 'Bedrooms', 'Bathrooms', 'Age']\n",
    "\n",
    "# looping over each subplot to plot data\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        index = 2*i + j  # calculating the index\n",
    "        axs[i, j].scatter(x_values[:,index], k_train, marker='o', color='red', label='Original data')\n",
    "        axs[i, j].scatter(x_values[:,index], rounded_predictions_lib, marker='+', color='black', label='SciKit Learn')\n",
    "        axs[i, j].scatter(x_values[:,index], rounded_predictions, marker='x', color='green', label='Custom Model')\n",
    "        axs[i, j].set_xlabel(features[index])\n",
    "        axs[i, j].set_ylabel('Price')\n",
    "        axs[i, j].legend()\n",
    "\n",
    "# displaying the figure with subplots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a296103",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}