Logistic_.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQa3adwq2U8O"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "xuT0uxmZTn15"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "xs1uy9XIr4Bk"
      },
      "outputs": [],
      "source": [
        "# Importing the libraries needed\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as pp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "L3WuGAsgqoD2"
      },
      "outputs": [],
      "source": [
        "# Loading the Dataset for the Logistic regression \n",
        "data_5 = pd.read_csv('emnist-letters-train.csv',header=None)\n",
        "data_5 = np.array(data_5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "9rG7tAVyq2zd"
      },
      "outputs": [],
      "source": [
        "# Seperating Labels and its corresponding values \n",
        "\n",
        "# For label\n",
        "m,n= data_5.shape\n",
        "y_5 = data_5[:,0:1].reshape(m,1)\n",
        "\n",
        "# Seperating x features\n",
        "\n",
        "X_7 = data_5[:,1:n]\n",
        "ones = np.ones([X_7.shape[0],1]) #This is done because for convinience X_0 is considered to have entering of only 1(for simplicity)\n",
        "X_7 = np.concatenate ((ones , X_7) , axis=1)\n",
        "\n",
        "\n",
        "# Setting theta matrice\n",
        "theta = np.zeros([n,26])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "ixomjLW5rPLE"
      },
      "outputs": [],
      "source": [
        "#@title Setting Hyperparameters\n",
        "Alpha = 0.00001 #@param {type:\"slider\", min:0, max:1, step:0.0000000001}\n",
        "iters =  500    #@param {type:\"slider\", min:100, max:3000, step:100}\n",
        "alpha_4 = Alpha\n",
        "iters_4 = iters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "91b4GvL8rUTG"
      },
      "outputs": [],
      "source": [
        "# Defining the sigmoid function which will help us in obtaining the value of y in between 0 and 1 \n",
        "def sigmoid(z):\n",
        "  h_x = 1 / (1 + np.exp(-1*z))\n",
        "  return h_x "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "wao_cXWErcaU"
      },
      "outputs": [],
      "source": [
        "#Defining the cost function \n",
        "def computeCost(X,y,theta):\n",
        "    z = np.dot(X,theta)\n",
        "    h_x = sigmoid(z)\n",
        "    tobesummed = np.multiply(-y, np.log(h_x))-np.multiply((1-y),np.log(1-h_x))   \n",
        "    return np.sum(tobesummed)/(len(X))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "oj1uQ7FjbTnQ"
      },
      "outputs": [],
      "source": [
        "# defining the gradient descent function \n",
        "def gradientDescent(X,y,iters,alpha):\n",
        "    # global z\n",
        "    m,n=X.shape\n",
        "    theta=np.zeros((n,26))\n",
        "    cost = np.zeros(iters)\n",
        "    for i in range(iters):\n",
        "        z= np.dot(X,theta)\n",
        "        h_x=sigmoid(z)\n",
        "        theta = theta - ((alpha/len(X))*(np.dot(X.T,(h_x - y))))\n",
        "        cost[i]= computeCost(X, y, theta)\n",
        "        print(cost[i])\n",
        "        pp.scatter(i,cost[i])\n",
        "\n",
        "    return theta,cost,h_x\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SWaDLPXfbdTL",
        "outputId": "ae9237c0-78ed-4968-a4fe-5d058f34c057"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "2.144035866702907\n",
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            "2.1437484434236955\n",
            "2.1436054328337915\n",
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            "2.142757027673147\n",
            "2.1426172138050275\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Making the assignment more engaging by adding in a graph \n",
        "def Ymaker(vector):\n",
        "    m=vector.size\n",
        "    L = np.zeros((m,26))\n",
        "    k =0\n",
        "    for i in vector:\n",
        "        L[k][i-1] = 1\n",
        "        k += 1\n",
        "\n",
        "    return L\n",
        "\n",
        "L=Ymaker(y_5)\n",
        "g,J,y_pred=gradientDescent(X_7,L,iters_4,alpha_4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "vs0AnuwnboM-"
      },
      "outputs": [],
      "source": [
        "# defining a function to check the accuracy of our model \n",
        "def accuracy(y_pred,y_actual):\n",
        "    count=0\n",
        "    m=y_pred.size\n",
        "    for i in range(m):\n",
        "        if(y_pred[i]==y_actual[i]):\n",
        "            count+=1\n",
        "    return (count/m)*100\n",
        "\n",
        "# The predicted y are represented in the form of 0s and 1s \n",
        "# So defining a function to convert them back to their original values\n",
        "def prediction(matrix):\n",
        "    \n",
        "    m,n = matrix.shape\n",
        "    h = np.zeros((m,1))\n",
        "    for i in range(m):\n",
        "        a_sliced = matrix[i]\n",
        "        index =np.where(a_sliced == np.amax(a_sliced))[0]\n",
        "#         print((index))\n",
        "        h[i] = (index+1)\n",
        "            \n",
        "    return h"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "38zb2JvHbsoe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1dceb632-81cd-4002-cd5c-769f3b0e6c9f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11.],\n",
              "       [ 7.],\n",
              "       [16.],\n",
              "       ...,\n",
              "       [ 1.],\n",
              "       [23.],\n",
              "       [ 9.]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "y_pred=prediction(y_pred)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "5gbWyd6CbvoF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61b91c25-c3c9-4053-8139-1b5f6c5e0bbb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "68.55405405405406"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "accuracy(y_pred,y_5)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing"
      ],
      "metadata": {
        "id": "vUSiBdU-TgjI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "7pS065qtejME",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "a7c4dac5-7a84-47ad-a8e7-52f551c69d58"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "\n",
              "[14800 rows x 785 columns]"
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              "\n",
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              "        vertical-align: middle;\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>14800 rows × 785 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04979db2-a50c-4233-abe5-650dac44a04f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-04979db2-a50c-4233-abe5-650dac44a04f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-04979db2-a50c-4233-abe5-650dac44a04f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "data_6 = pd.read_csv('emnist-letters-test.csv',header =None )\n",
        "data_6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "AAvh6gQ4g8f5"
      },
      "outputs": [],
      "source": [
        "# Seperating Labels and its corresponding values \n",
        "\n",
        "# For labels\n",
        "m,n= data_6.shape\n",
        "y_6 = data_6.iloc[:,0:1]\n",
        "\n",
        "# Seperating x features\n",
        "\n",
        "X_8 = data_6.iloc[:,1:n]\n",
        "ones = np.ones([X_8.shape[0],1]) #This is done because for convinience X_0 is considered to have entering of only 1(for simplicity)\n",
        "X_8 = np.concatenate ((ones , X_8) , axis=1)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "A9vUeoxwb-QL"
      },
      "outputs": [],
      "source": [
        "y_pred_of_test=prediction(sigmoid(X_8@g))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "X8OKXcSrkmfr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57398d11-84ae-4ded-ec02-720e1d929e6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14800\n",
            "14800\n"
          ]
        }
      ],
      "source": [
        "print(len(y_pred_of_test))\n",
        "print(len(y_6))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_6 = np.array(y_6)"
      ],
      "metadata": {
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      "execution_count": 19,
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              "66.8918918918919"
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          "metadata": {},
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      ],
      "source": [
        "accuracy(y_pred_of_test,y_6)"
      ]
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      "name": "Logistic .ipynb",
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