Lab 25-26 - Ridge and Lasso Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af42d569",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60428b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's fetch the dataset\n",
    "X, y = fetch_california_housing(as_frame=True, return_X_y = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "115d97fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32e46cd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let us split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e7c3e9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the pipeline without any regularization\n",
    "clf_pl = Pipeline([(\"feature_scaling\", StandardScaler()),\n",
    "                           (\"lin_reg\", LinearRegression())]\n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5e0322b",
   "metadata": {},
   "outputs": [
    {
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       "                (&#x27;lin_reg&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;feature_scaling&#x27;, StandardScaler()),\n",
       "                (&#x27;lin_reg&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
      ],
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       "Pipeline(steps=[('feature_scaling', StandardScaler()),\n",
       "                ('lin_reg', LinearRegression())])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_pl.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "989596db",
   "metadata": {},
   "outputs": [
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       "0.609873031052925"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_pl.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4ddb9a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.591050979549135"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_pl.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "af5e4ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us now use polynomial features of degree 2\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f12f204b",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_clf_pl = Pipeline([(\"feature_scaling\", StandardScaler()),\n",
    "                   (\"poly\", PolynomialFeatures(degree=2)),\n",
    "                   (\"lin_reg\", LinearRegression())]\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "25e4c1a2",
   "metadata": {},
   "outputs": [
    {
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       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;feature_scaling&#x27;, StandardScaler()),\n",
       "                (&#x27;poly&#x27;, PolynomialFeatures()),\n",
       "                (&#x27;lin_reg&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;feature_scaling&#x27;, StandardScaler()),\n",
       "                (&#x27;poly&#x27;, PolynomialFeatures()),\n",
       "                (&#x27;lin_reg&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('feature_scaling', StandardScaler()),\n",
       "                ('poly', PolynomialFeatures()),\n",
       "                ('lin_reg', LinearRegression())])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_clf_pl.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c0b27930",
   "metadata": {},
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    {
     "data": {
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       "0.6829089327528395"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_clf_pl.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e0f3b023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.65630058807575"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_clf_pl.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ec4d79",
   "metadata": {},
   "source": [
    "## 1. Ridge Regression (L2 Regularization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f496ce26",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dfe79c0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "alpha_values = np.logspace(-3, 3, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c392281e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d007cd6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc = []\n",
    "test_acc = []\n",
    "\n",
    "for av in alpha_values:\n",
    "    ridge_ppl = Pipeline([\n",
    "        ('fs', StandardScaler()),\n",
    "        ('poly', PolynomialFeatures(2)),\n",
    "        ('ridge_reg', Ridge(alpha= av))\n",
    "    ])\n",
    "    \n",
    "    ridge_ppl.fit(X_train, y_train)\n",
    "    train_acc.append(ridge_ppl.score(X_train, y_train))\n",
    "    test_acc.append(ridge_ppl.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6f905b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(alpha_values, train_acc, label='Train Accuracy')\n",
    "plt.plot(alpha_values, test_acc, label='Test Accuracy')\n",
    "\n",
    "plt.legend()\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "93b11acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6731557498332299, 0.6673572223889983)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_ppl = Pipeline([\n",
    "        ('fs', StandardScaler()),\n",
    "        ('poly', PolynomialFeatures(2)),\n",
    "        ('ridge_reg', Ridge(alpha= 100))\n",
    "    ])\n",
    "    \n",
    "ridge_ppl.fit(X_train, y_train)\n",
    "ridge_ppl.score(X_train, y_train), ridge_ppl.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf1c529a",
   "metadata": {},
   "source": [
    "## 2. Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e6c0290",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c12c4a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MuhammadWasim\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.467e+00, tolerance: 2.066e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "train_acc = []\n",
    "test_acc = []\n",
    "\n",
    "for av in alpha_values:\n",
    "    lasso_ppl = Pipeline([\n",
    "        ('fs', StandardScaler()),\n",
    "        ('poly', PolynomialFeatures(2)),\n",
    "        ('lasso_reg', Lasso(alpha= av, max_iter=50000))\n",
    "    ])\n",
    "    \n",
    "    lasso_ppl.fit(X_train, y_train)\n",
    "    train_acc.append(lasso_ppl.score(X_train, y_train))\n",
    "    test_acc.append(lasso_ppl.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b9165908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(alpha_values, train_acc, label='Train Accuracy')\n",
    "plt.plot(alpha_values, test_acc, label='Test Accuracy')\n",
    "\n",
    "plt.legend()\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fa8e8426",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MuhammadWasim\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.467e+00, tolerance: 2.066e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.6806306925586114, 0.6784351047363308)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_ppl = Pipeline([\n",
    "        ('fs', StandardScaler()),\n",
    "        ('poly', PolynomialFeatures(2)),\n",
    "        ('lasso_reg', Lasso(alpha= 0.001, max_iter=50000))\n",
    "    ])\n",
    "    \n",
    "lasso_ppl.fit(X_train, y_train)\n",
    "\n",
    "lasso_ppl.score(X_train, y_train), lasso_ppl.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1af54df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(lasso_ppl['lasso_reg'].coef_ != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c6be9df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(lasso_ppl['lasso_reg'].coef_ == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6efbe2b1",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}