Day_59_ridge_regression_gradient_descent.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Manish-k723/100-days-of-ML/blob/main/Day_59_ridge_regression_gradient_descent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "0a64ff79",
      "metadata": {
        "id": "0a64ff79"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_diabetes\n",
        "from sklearn.metrics import r2_score\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "f5d0adc9",
      "metadata": {
        "id": "f5d0adc9"
      },
      "outputs": [],
      "source": [
        "X,y = load_diabetes(return_X_y=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "877a6fcc",
      "metadata": {
        "id": "877a6fcc"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "f356594b",
      "metadata": {
        "id": "f356594b"
      },
      "outputs": [],
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "7353416f",
      "metadata": {
        "id": "7353416f"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import SGDRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "dd9147db",
      "metadata": {
        "id": "dd9147db"
      },
      "outputs": [],
      "source": [
        "reg = SGDRegressor(penalty='l2',max_iter=500,eta0=0.1,learning_rate='constant',alpha=0.001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "bc87d135",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bc87d135",
        "outputId": "458a37a9-1d55-4699-ff31-7b27fa3bb43e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score 0.4501849274954637\n",
            "[  43.63597601 -163.61954786  374.16665856  273.6359228    -4.97531752\n",
            "  -58.89663366 -166.71928687  137.61294055  336.49073175   92.85197852]\n",
            "[148.3191795]\n"
          ]
        }
      ],
      "source": [
        "reg.fit(X_train,y_train)\n",
        "\n",
        "y_pred = reg.predict(X_test)\n",
        "print(\"R2 score\",r2_score(y_test,y_pred))\n",
        "print(reg.coef_)\n",
        "print(reg.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "d9b329c6",
      "metadata": {
        "id": "d9b329c6"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "reg = Ridge(alpha=0.001, max_iter=500,solver='sparse_cg')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "67492f20",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67492f20",
        "outputId": "441edb37-4393-4eab-97b1-81e5bb4ac7b6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score 0.46250101619914563\n",
            "[  34.52192544 -290.84084076  482.40181344  368.0678662  -852.44873179\n",
            "  501.59160336  180.11115788  270.76333979  759.73534372   37.4913546 ]\n",
            "151.10198517439466\n"
          ]
        }
      ],
      "source": [
        "reg.fit(X_train,y_train)\n",
        "\n",
        "y_pred = reg.predict(X_test)\n",
        "print(\"R2 score\",r2_score(y_test,y_pred))\n",
        "print(reg.coef_)\n",
        "print(reg.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "9c215108",
      "metadata": {
        "id": "9c215108"
      },
      "outputs": [],
      "source": [
        "class MeraRidgeGD:\n",
        "    \n",
        "    def __init__(self,epochs,learning_rate,alpha):\n",
        "        \n",
        "        self.learning_rate = learning_rate\n",
        "        self.epochs = epochs\n",
        "        self.alpha = alpha\n",
        "        self.coef_ = None\n",
        "        self.intercept_ = None\n",
        "        \n",
        "    def fit(self,X_train,y_train):\n",
        "        \n",
        "        self.coef_ = np.ones(X_train.shape[1])\n",
        "        self.intercept_ = 0\n",
        "        thetha = np.insert(self.coef_,0,self.intercept_)\n",
        "        \n",
        "        X_train = np.insert(X_train,0,1,axis=1)\n",
        "        \n",
        "        for i in range(self.epochs):\n",
        "            thetha_der = np.dot(X_train.T,X_train).dot(thetha) - np.dot(X_train.T,y_train) + self.alpha*thetha\n",
        "            thetha = thetha - self.learning_rate*thetha_der\n",
        "        \n",
        "        self.coef_ = thetha[1:]\n",
        "        self.intercept_ = thetha[0]\n",
        "    \n",
        "    def predict(self,X_test):\n",
        "        \n",
        "        return np.dot(X_test,self.coef_) + self.intercept_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "12040494",
      "metadata": {
        "id": "12040494"
      },
      "outputs": [],
      "source": [
        "reg = MeraRidgeGD(epochs=500,alpha=0.001,learning_rate=0.005)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "cd5d4820",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cd5d4820",
        "outputId": "14d255b3-6586-43dd-eb33-4a2f8682aed6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score 0.4738018280260913\n",
            "[  46.65050914 -221.3750037   452.12080647  325.54248128  -29.09464178\n",
            "  -96.47517735 -190.90017011  146.32900372  400.80267299   95.09048094]\n",
            "150.86975316713472\n"
          ]
        }
      ],
      "source": [
        "reg.fit(X_train,y_train)\n",
        "\n",
        "y_pred = reg.predict(X_test)\n",
        "print(\"R2 score\",r2_score(y_test,y_pred))\n",
        "print(reg.coef_)\n",
        "print(reg.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "629a5361",
      "metadata": {
        "id": "629a5361"
      },
      "outputs": [],
      "source": []
    }
  ],
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