Day_58_ridge_regression_from_scratch.ipynb

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      "id": "99dcba6d",
      "metadata": {
        "id": "99dcba6d"
      },
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      "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": "821f3faf",
      "metadata": {
        "id": "821f3faf"
      },
      "outputs": [],
      "source": [
        "X,y = load_diabetes(return_X_y=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "741b4551",
      "metadata": {
        "id": "741b4551"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "5d4d2d1b",
      "metadata": {
        "id": "5d4d2d1b"
      },
      "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": "30a774ef",
      "metadata": {
        "id": "30a774ef"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import Ridge"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "5a15c5fe",
      "metadata": {
        "id": "5a15c5fe"
      },
      "outputs": [],
      "source": [
        "reg = Ridge(alpha=0.1,solver='cholesky')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "7d66d9f4",
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          "data": {
            "text/plain": [
              "Ridge(alpha=0.1, solver='cholesky')"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "reg.fit(X_train,y_train)"
      ]
    },
    {
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      "execution_count": 8,
      "id": "09460a79",
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              "0.4693128853309805"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "y_pred = reg.predict(X_test)\n",
        "r2_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "927958f9",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "927958f9",
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          "text": [
            "[  44.02063391 -241.69329987  452.98665299  332.04420177  -76.33686744\n",
            "  -68.52143809 -164.98809083  149.96908118  431.61347417   58.51862681]\n",
            "150.89053425602808\n"
          ]
        }
      ],
      "source": [
        "print(reg.coef_)\n",
        "print(reg.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "7ac16788",
      "metadata": {
        "id": "7ac16788"
      },
      "outputs": [],
      "source": [
        "class MeraRidge:\n",
        "    \n",
        "    def __init__(self,alpha=0.1):\n",
        "        \n",
        "        self.alpha = alpha\n",
        "        self.coef_ = None\n",
        "        self.intercept_ = None\n",
        "        \n",
        "    def fit(self,X_train,y_train):\n",
        "        \n",
        "        X_train = np.insert(X_train,0,1,axis=1)\n",
        "        I = np.identity(X_train.shape[1])\n",
        "        I[0][0] = 0\n",
        "        result = np.linalg.inv(np.dot(X_train.T,X_train) + self.alpha * I).dot(X_train.T).dot(y_train)\n",
        "        self.intercept_ = result[0]\n",
        "        self.coef_ = result[1:]\n",
        "    \n",
        "    def predict(self,X_test):\n",
        "        return np.dot(X_test,self.coef_) + self.intercept_"
      ]
    },
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      "execution_count": 11,
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        "id": "0884ea38",
        "outputId": "284cc427-7e64-4c86-fc5f-36bd94ead286"
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          "name": "stdout",
          "text": [
            "0.4693128853309805\n",
            "[  44.02063391 -241.69329987  452.98665299  332.04420177  -76.33686744\n",
            "  -68.52143809 -164.98809083  149.96908118  431.61347417   58.51862681]\n",
            "150.8905342560281\n"
          ]
        }
      ],
      "source": [
        "reg = MeraRidge()\n",
        "reg.fit(X_train,y_train)\n",
        "y_pred = reg.predict(X_test)\n",
        "print(r2_score(y_test,y_pred))\n",
        "print(reg.coef_)\n",
        "print(reg.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "807e38bc",
      "metadata": {
        "id": "807e38bc"
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      "outputs": [],
      "source": []
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