classification-logistic-regression-from-scratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "91a8dd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import copy\n",
    "from math import log, ceil\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0ec40d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width         species\n",
       "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
       "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\n",
    "    './data/iris.data',\n",
    "    names=[\n",
    "        \"sepal_length\",\n",
    "        \"sepal_width\",\n",
    "        \"petal_length\",\n",
    "        \"petal_width\",\n",
    "        \"species\"\n",
    "    ]\n",
    ")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "eb50f547",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>150.0</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.80</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.35</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>150.0</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count      mean       std  min  25%   50%  75%  max\n",
       "sepal_length  150.0  5.843333  0.828066  4.3  5.1  5.80  6.4  7.9\n",
       "sepal_width   150.0  3.054000  0.433594  2.0  2.8  3.00  3.3  4.4\n",
       "petal_length  150.0  3.758667  1.764420  1.0  1.6  4.35  5.1  6.9\n",
       "petal_width   150.0  1.198667  0.763161  0.1  0.3  1.30  1.8  2.5"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"stats\")\n",
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "133254d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records:  150\n",
      "Records of each species: \n",
      "Iris-setosa        50\n",
      "Iris-versicolor    50\n",
      "Iris-virginica     50\n",
      "Name: species, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Total records: \", data.shape[0])\n",
    "print(\"Records of each species: \", data.species.value_counts(), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ee24ec27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encoding for species\n",
    "data['species'] = data['species'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "553a74e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature & Target Selection\n",
    "features = data.select_dtypes('float').columns\n",
    "target = ['species']\n",
    "\n",
    "# Feature& Target  Dataset\n",
    "X = data[features]\n",
    "y = data[target]\n",
    "\n",
    "# Split into 90% train and 10% test\n",
    "test_size = 0.1\n",
    "\n",
    "# splitting into training and test data set\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=test_size,\n",
    "    random_state=0\n",
    ")\n",
    "X_test = X_test.reset_index(drop=True)\n",
    "y_test = y_test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "63bb5c14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training features shape: (135, 4)\n",
      "Training targets shape: (135, 1)\n",
      "Testing features shape: (15, 4)\n",
      "Testing targets shape: (15, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"Training features shape: {X_train.shape}\",\n",
    "    f\"Training targets shape: {y_train.shape}\",\n",
    "    f\"Testing features shape: {X_test.shape}\",\n",
    "    f\"Testing targets shape: {y_test.shape}\",\n",
    "    sep='\\n'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f5ea6670",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five elements in X_train are: \n",
      "     sepal_length  sepal_width  petal_length  petal_width\n",
      "37           4.9          3.1           1.5          0.1\n",
      "78           6.0          2.9           4.5          1.5\n",
      "90           5.5          2.6           4.4          1.2\n",
      "45           4.8          3.0           1.4          0.3\n",
      "16           5.4          3.9           1.3          0.4\n",
      "First five elements in Y_train are: \n",
      "     species\n",
      "37        0\n",
      "78        1\n",
      "90        1\n",
      "45        0\n",
      "16        0\n"
     ]
    }
   ],
   "source": [
    "print(\"First five elements in X_train are: \\n\", X_train.iloc[:5])\n",
    "print(\"First five elements in Y_train are: \\n\", y_train.iloc[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "83651ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2887290d0>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1062.74x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data Exploration & Visualisation\n",
    "\n",
    "# Plotting a pair-grid to compare features and check if this data is suited for\n",
    "# classification, or some more feature engineering is needed\n",
    "sns.PairGrid(X_train.assign(species=y_train), hue='species').map(plt.scatter).add_legend()\n",
    "\n",
    "# sns.FacetGrid(data, hue='species', height = 5).map(plt.scatter, 'sepal_length','sepal_width').add_legend()\n",
    "# sns.FacetGrid(data, hue='species', height = 5).map(plt.scatter, 'sepal_length', 'petal_width').add_legend()\n",
    "# sns.FacetGrid(data, hue='species', height = 5).map(plt.scatter, 'petal_length','sepal_width').add_legend()\n",
    "# sns.FacetGrid(data, hue='species', height = 5).map(plt.scatter, 'petal_length','petal_width').add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "084e38d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inference from above data -\n",
    "# This data has 3 unique categories, thus we would have to make 3 Logistic Regression models\n",
    "# where we take 1 class and group the other 2\n",
    "# ('Iris-setosa' & 'Iris-versicolor', 'Iris-virginica')\n",
    "# ('Iris-setosa' & 'Iris-virginica', 'Iris-versicolor')\n",
    "# ('Iris-versicolor' & 'Iris-virginica', 'Iris-setosa')\n",
    "\n",
    "# This way, we can classify three classes using only binary classification\n",
    "# Note - Multinomial classification is also possible, but it performs better\n",
    "# when done via Nueral Networks with the SoftMax Activation Function\n",
    "\n",
    "# Moreover from only plotting 2 features on a 2D graph, clear clusters are visible in the ScatterPlot,\n",
    "# due to which this problem could also be solved with clustering methods and algorithms like RandomForest or KNN\n",
    "\n",
    "# There's a clear distinction from the \"petal_width\" parameter (and also the petal_length param),\n",
    "# the petal widths of iris-setosa, iris-versicolor and iris-virginica are in asceding order\n",
    "\n",
    "# => intuition - the weight for 'petal_width' param would be higher than that of the other params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b4a6bb4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression Model\n",
    "# f_wb (x) = g(w.x + b), where g is the sigmoid function (1 / (1 + e^-z))\n",
    "\n",
    "def sigmoid(z):\n",
    "    g = 1 / (1 + np.exp(-z))\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "2d930f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cost Function\n",
    "# The cost function for logistic regression is:\n",
    "# J(w, b) = (1 / m) * summation(loss(f_wb(x_i), y_i))\n",
    "# where m = no of inputs, f_wb is the model, x_i is the ith\n",
    "# set of input features, and y_i is the actual target value\n",
    "# f_wb(x_i) gives the predicted value of target for given\n",
    "# input features\n",
    "# f_wb(x_i) = g(w.x_i + b), where g is the sigmoid function\n",
    "# The loss function for binary classification is:\n",
    "# -y_i * log(f_wb(x_i)) - (1 - y_i) * log(1 - f_wb(x_i))\n",
    "def compute_cost(X, y, w, b, *argv):\n",
    "    m, n = X.shape\n",
    "    total_cost = 0\n",
    "    \n",
    "    for i in range(m):\n",
    "        f_wb_i = sigmoid(np.dot(w, X.iloc[i]) + b)\n",
    "        total_cost += (- y.iloc[i] * log(f_wb_i) - (1 - y.iloc[i]) * log(1 - f_wb_i))[0]\n",
    "        \n",
    "    return total_cost / m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d504e0fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def altered_cost(X, y, w, b, *argv):\n",
    "    m = y.shape[0]\n",
    "\n",
    "    y = y.values.flatten()\n",
    "    f_wb = sigmoid(np.dot(X, w) + b)\n",
    "    total_cost = -np.sum(y * np.log(f_wb) + (1 - y) * np.log(1 - f_wb))\n",
    "        \n",
    "    return total_cost / m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "946657c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def isolate_single_category(y, category):\n",
    "    y_filtered = y.copy()\n",
    "    \n",
    "    for i in range(y_filtered.shape[0]):\n",
    "        if (y_filtered.iloc[i][0] != category):\n",
    "            y_filtered.iloc[i] = 0\n",
    "        else:\n",
    "            y_filtered.iloc[i] = 1\n",
    "\n",
    "    return y_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f529d243",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iris-setosa) Cost at initial w and b (zeros): 0.693\n",
      "(Iris-versicolor) Cost at initial w and b (zeros): 0.693\n",
      "(Iris-virginica) Cost at initial w and b (zeros): 0.693\n"
     ]
    }
   ],
   "source": [
    "m, n = X_train.shape\n",
    "\n",
    "# Compute and display cost with w and b initialized to zeros\n",
    "initial_w = np.zeros(n)\n",
    "initial_b = 0.\n",
    "\n",
    "# Pre-Process Y_train into 3 categories (using one-hot-encoding)\n",
    "y_train_setosa = isolate_single_category(y_train, 0)\n",
    "y_train_versicolor = isolate_single_category(y_train, 1)\n",
    "y_train_virginica = isolate_single_category(y_train, 2)\n",
    "\n",
    "\n",
    "cost_setosa = altered_cost(X_train, y_train_setosa, initial_w, initial_b)\n",
    "print('(Iris-setosa) Cost at initial w and b (zeros): {:.3f}'.format(cost_setosa))\n",
    "\n",
    "cost_versicolor = altered_cost(X_train, y_train_versicolor, initial_w, initial_b)\n",
    "print('(Iris-versicolor) Cost at initial w and b (zeros): {:.3f}'.format(cost_versicolor))\n",
    "\n",
    "cost_virginica = altered_cost(X_train, y_train_virginica, initial_w, initial_b)\n",
    "print('(Iris-virginica) Cost at initial w and b (zeros): {:.3f}'.format(cost_virginica))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "a73f0cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_gradient(X, y, w, b, *argv): \n",
    "    m, n = X.shape\n",
    "    dj_dw = np.zeros(w.shape)\n",
    "    dj_db = 0\n",
    "\n",
    "    err = []\n",
    "    for i in range(m):\n",
    "        f_wb_i = sigmoid(np.dot(w, X.iloc[i]) + b)\n",
    "\n",
    "        err_i = f_wb_i - y.iloc[i]   \n",
    "        \n",
    "        for j in range(n):\n",
    "            dj_dw[j] += err_i * X.iloc[i, j]\n",
    "            \n",
    "        dj_db += err_i\n",
    "   \n",
    "    dj_dw = dj_dw / m\n",
    "    dj_db = dj_db / m\n",
    "\n",
    "    return dj_db, dj_dw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c5512b37",
   "metadata": {},
   "outputs": [],
   "source": [
    "def altered_gradient(X, y, w, b, *argv): \n",
    "    m, n = X.shape\n",
    "    \n",
    "    f_wb = sigmoid(np.dot(X, w) + b)\n",
    "\n",
    "    err = f_wb - y.values.flatten()\n",
    " \n",
    "    dj_dw = np.dot(X.T, err) / m\n",
    "    dj_db = np.sum(err) / m\n",
    "\n",
    "    return dj_db, dj_dw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ce707fcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Iris-setosa: \n",
      "dj_db at initial w and b (zeros):0.15185185185185185\n",
      "dj_dw at initial w and b (zeros):[1.1674074074074072, 0.34518518518518515, 1.3429629629629631, 0.5081481481481481]\n",
      "\n",
      "For Iris-versicolor: \n",
      "dj_db at initial w and b (zeros):0.18888888888888888\n",
      "dj_dw at initial w and b (zeros):[1.0837037037037038, 0.6703703703703704, 0.5429629629629631, 0.18222222222222217]\n",
      "\n",
      "For Iris-virginica: \n",
      "dj_db at initial w and b (zeros):0.15925925925925927\n",
      "dj_dw at initial w and b (zeros):[0.6555555555555558, 0.5133333333333332, -0.031851851851851874, -0.09703703703703703]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "initial_w = np.zeros(n)\n",
    "initial_b = 0.\n",
    "\n",
    "print(\"For Iris-setosa: \")\n",
    "dj_db, dj_dw = altered_gradient(X_train, y_train_setosa, initial_w, initial_b)\n",
    "print(f'dj_db at initial w and b (zeros):{dj_db}' )\n",
    "print(f'dj_dw at initial w and b (zeros):{dj_dw.tolist()}\\n' )\n",
    "\n",
    "print(\"For Iris-versicolor: \")\n",
    "dj_db, dj_dw = altered_gradient(X_train, y_train_versicolor, initial_w, initial_b)\n",
    "print(f'dj_db at initial w and b (zeros):{dj_db}' )\n",
    "print(f'dj_dw at initial w and b (zeros):{dj_dw.tolist()}\\n' )\n",
    "\n",
    "print(\"For Iris-virginica: \")\n",
    "dj_db, dj_dw = altered_gradient(X_train, y_train_virginica, initial_w, initial_b)\n",
    "print(f'dj_db at initial w and b (zeros):{dj_db}' )\n",
    "print(f'dj_dw at initial w and b (zeros):{dj_dw.tolist()}\\n' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "a9ee7303",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Learning parameters w, b so as to minimise the cost fuction J(w, b)\n",
    "# 'converging the value of the function'\n",
    "def gradient_descent(X, y, w_in, b_in, cost_function, gradient_function, alpha, num_iters, lambda_):\n",
    "    m = X.shape[0]\n",
    "    \n",
    "    J_history = []\n",
    "    w_history = []\n",
    "    \n",
    "    for i in range(num_iters):\n",
    "        # calculate gradient\n",
    "        dj_db, dj_dw = gradient_function(X, y, w_in, b_in, lambda_)\n",
    "        \n",
    "        # Update params w, b using gradient\n",
    "        w_in = w_in - alpha * dj_dw\n",
    "        b_in = b_in - alpha * dj_db\n",
    "        \n",
    "        # save cost J\n",
    "        if (i < 100000):\n",
    "            cost = cost_function(X, y, w_in, b_in, lambda_)\n",
    "            J_history.append(cost)\n",
    "        \n",
    "        # print cost every 10 intervals\n",
    "        if (i % ceil(num_iters / 10) == 0 or i == num_iters - 1):\n",
    "            w_history.append(w_in)\n",
    "            print(f\"Iteration {i:4}: Cost {float(J_history[-1]):8.3f}  \")\n",
    "\n",
    "    return w_in, b_in, J_history, w_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "04008bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "initial_w = 0.01 * (np.random.rand(4) - 0.5) # w has 4 features\n",
    "initial_b = 0\n",
    "iterations = 10000\n",
    "\n",
    "# learning rate\n",
    "alpha = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "c19cc9e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Gradient Descent for Iris-setosa classifier\n",
      "Iteration    0: Cost    0.682  \n",
      "Iteration 1000: Cost    0.329  \n",
      "Iteration 2000: Cost    0.219  \n",
      "Iteration 3000: Cost    0.163  \n",
      "Iteration 4000: Cost    0.130  \n",
      "Iteration 5000: Cost    0.108  \n",
      "Iteration 6000: Cost    0.093  \n",
      "Iteration 7000: Cost    0.081  \n",
      "Iteration 8000: Cost    0.072  \n",
      "Iteration 9000: Cost    0.065  \n",
      "Iteration 9999: Cost    0.059  \n",
      "w, b optimised from gradient_descent:  [ 0.28468052  0.91609968 -1.47909258 -0.68050164] 0.17677097011515674\n"
     ]
    }
   ],
   "source": [
    "print(\"Running Gradient Descent for Iris-setosa classifier\")\n",
    "w_s, b_s, J_history_s, _s = gradient_descent(X_train, y_train_setosa, initial_w, initial_b, altered_cost, altered_gradient, alpha, iterations, 0)\n",
    "\n",
    "print(\"w, b optimised from gradient_descent: \", w_s, b_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d2a8c5e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Gradient Descent for Iris-versicolor classifier\n",
      "Iteration    0: Cost    0.683  \n",
      "Iteration 2000: Cost    0.562  \n",
      "Iteration 4000: Cost    0.552  \n",
      "Iteration 6000: Cost    0.543  \n",
      "Iteration 8000: Cost    0.536  \n",
      "Iteration 10000: Cost    0.531  \n",
      "Iteration 12000: Cost    0.526  \n",
      "Iteration 14000: Cost    0.522  \n",
      "Iteration 16000: Cost    0.518  \n",
      "Iteration 18000: Cost    0.515  \n",
      "Iteration 19999: Cost    0.513  \n",
      "w, b optimised from gradient_descent:  [ 0.32453031 -1.43870784  0.6194038  -1.31069612] 0.7908500352606372\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.004\n",
    "iterations = 20000\n",
    "\n",
    "print(\"Running Gradient Descent for Iris-versicolor classifier\")\n",
    "w_vc, b_vc, J_history_vc, _vc = gradient_descent(X_train, y_train_versicolor, initial_w, initial_b, altered_cost, altered_gradient, alpha, iterations, 0)\n",
    "\n",
    "print(\"w, b optimised from gradient_descent: \", w_vc, b_vc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "4dab193f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Gradient Descent for Iris-virginica classifier\n",
      "Iteration    0: Cost    0.691  \n",
      "Iteration 2000: Cost    0.301  \n",
      "Iteration 4000: Cost    0.250  \n",
      "Iteration 6000: Cost    0.221  \n",
      "Iteration 8000: Cost    0.201  \n",
      "Iteration 10000: Cost    0.186  \n",
      "Iteration 12000: Cost    0.174  \n",
      "Iteration 14000: Cost    0.164  \n",
      "Iteration 16000: Cost    0.156  \n",
      "Iteration 18000: Cost    0.150  \n",
      "Iteration 19999: Cost    0.144  \n",
      "w, b optimised from gradient_descent:  [-1.818155   -1.7532863   2.74516407  2.53003581] -1.1203875430648895\n"
     ]
    }
   ],
   "source": [
    "print(\"Running Gradient Descent for Iris-virginica classifier\")\n",
    "w_vg, b_vg, J_history_vg, _vg = gradient_descent(X_train, y_train_virginica, initial_w, initial_b, altered_cost, altered_gradient, alpha, iterations, 0)\n",
    "\n",
    "print(\"w, b optimised from gradient_descent: \", w_vg, b_vg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "c9c1f264",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(X, w_s, b_s, w_vc, b_vc, w_vg, b_vg): \n",
    "    # number of training examples\n",
    "    m, n = X.shape   \n",
    "    p = np.zeros(m, dtype=object)\n",
    "   \n",
    "    for i in range(m):   \n",
    "        z_wb_s = np.dot(w_s, X.iloc[i]) + b_s\n",
    "        z_wb_vc = np.dot(w_vc, X.iloc[i]) + b_vc\n",
    "        z_wb_vg = np.dot(w_vg, X.iloc[i]) + b_vg\n",
    "        \n",
    "        # Calculate the prediction for setosaa\n",
    "        f_wb_s = sigmoid(z_wb_s)\n",
    "        \n",
    "        # Calculate the prediction for versicolor\n",
    "        f_wb_vc = sigmoid(z_wb_vc)\n",
    "        \n",
    "        # Calculate the prediction for virginica\n",
    "        f_wb_vg = sigmoid(z_wb_vg)\n",
    "\n",
    "        f_wb = [f_wb_s, f_wb_vc, f_wb_vg]\n",
    "        p[i] = f_wb.index(max(f_wb))\n",
    "        \n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "74711516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1, 1], dtype=object)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test against 2 cases\n",
    "X_ = pd.DataFrame([[5.9, 3.0, 5.1, 1.8], [5.0, 3.6, 1.4, 0.2], [6.7,3.1,4.4, 1.4], [6.0,2.9,4.5,1.5]])\n",
    "predict(X_, w_s, b_s, w_vc, b_vc, w_vg, b_vg)\n",
    "\n",
    "# outputs seem to be accurate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "8085a571",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 97.037037\n"
     ]
    }
   ],
   "source": [
    "p = predict(X_train, w_s, b_s, w_vc, b_vc, w_vg, b_vg)\n",
    "print('Train Accuracy: %f'%(np.mean(p == y_train.values.flatten()) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "43d34422",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 100.000000\n"
     ]
    }
   ],
   "source": [
    "p = predict(X_test, w_s, b_s, w_vc, b_vc, w_vg, b_vg)\n",
    "print('Test Accuracy: %f'%(np.mean(p == y_test.values.flatten()) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "331d7d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96.29629629629629"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using sklearn on the dataset\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "model = SVC()\n",
    "\n",
    "ovr = OneVsRestClassifier(model)\n",
    "\n",
    "#fit model to training data\n",
    "ovr.fit(X_train, y_train)\n",
    "\n",
    "#Predications\n",
    "ovr_pred = ovr.predict(X_train)\n",
    "\n",
    "#Adding Predictions to Test Dataset\n",
    "ovr_df = X_train.copy()\n",
    "ovr_df.insert(4,\"Actual\",y_train, True)\n",
    "ovr_df.insert(5,\"Predicted\",ovr_pred, True)\n",
    "\n",
    "100 * np.sum(ovr_df['Actual'] == ovr_df['Predicted']) / ovr_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a502e656",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls = [np.append(w_s,b_s), np.append(w_vc, b_vc), np.append(w_vg, b_vg)]\n",
    "model = pd.DataFrame(np.vstack(ls), columns=[\n",
    "        \"sepal_length_weight\",\n",
    "        \"sepal_width_weight\",\n",
    "        \"petal_length_weight\",\n",
    "        \"petal_width_weight\",\n",
    "        \"bias\"\n",
    "], index=pd.Index([\"iris-setosa\", \"iris-versicolor\", \"iris-virginica\"], name=\"category\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "fc1e1497",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.to_pickle('./data/Iris-LR-scratch.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "30b9f210",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.read_pickle('./data/Iris-LR-scratch.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "8b5a7ead",
   "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>sepal_length_weight</th>\n",
       "      <th>sepal_width_weight</th>\n",
       "      <th>petal_length_weight</th>\n",
       "      <th>petal_width_weight</th>\n",
       "      <th>bias</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>iris-setosa</th>\n",
       "      <td>0.284681</td>\n",
       "      <td>0.916100</td>\n",
       "      <td>-1.479093</td>\n",
       "      <td>-0.680502</td>\n",
       "      <td>0.176771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iris-versicolor</th>\n",
       "      <td>0.324530</td>\n",
       "      <td>-1.438708</td>\n",
       "      <td>0.619404</td>\n",
       "      <td>-1.310696</td>\n",
       "      <td>0.790850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iris-virginica</th>\n",
       "      <td>-1.818155</td>\n",
       "      <td>-1.753286</td>\n",
       "      <td>2.745164</td>\n",
       "      <td>2.530036</td>\n",
       "      <td>-1.120388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sepal_length_weight  sepal_width_weight  petal_length_weight  \\\n",
       "category                                                                        \n",
       "iris-setosa                 0.284681            0.916100            -1.479093   \n",
       "iris-versicolor             0.324530           -1.438708             0.619404   \n",
       "iris-virginica             -1.818155           -1.753286             2.745164   \n",
       "\n",
       "                 petal_width_weight      bias  \n",
       "category                                       \n",
       "iris-setosa               -0.680502  0.176771  \n",
       "iris-versicolor           -1.310696  0.790850  \n",
       "iris-virginica             2.530036 -1.120388  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "050006d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 2., 2., 2.])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([2, 2, 2, 2], dtype=\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e558776e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.28468052,  0.91609968, -1.47909258, -0.68050164])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.loc[\"iris-setosa\"][:4].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "5d88fe8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.7408570668356487"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(p.loc[\"iris-setosa\"][:4].to_numpy(), np.array([2, 2, 2, 2], dtype=\"float64\")) + p.loc[\"iris-setosa\"][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d73c8e98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "684d8c9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4752b870",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "444ab479",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61171f88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3aff6887",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cde61f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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