{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 15: Machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Robert Johansson\n",
    "\n",
    "Source code listings for [Numerical Python - Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib](https://link.springer.com/book/10.1007/979-8-8688-0413-7) (ISBN 979-8-8688-0412-0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn import model_selection\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn import neighbors\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn import ensemble\n",
    "from sklearn import cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['mathtext.fontset'] = 'stix'\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "mpl.rcParams['font.sans-serif'] = 'stix'\n",
    "\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Built in datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function sklearn.datasets._base.load_wine(*, return_X_y=False, as_frame=False)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.load_wine #()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function sklearn.datasets._california_housing.fetch_california_housing(*, data_home=None, download_if_missing=True, return_X_y=False, as_frame=False)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function sklearn.datasets._samples_generator.make_regression(n_samples=100, n_features=100, *, n_informative=10, n_targets=1, bias=0.0, effective_rank=None, tail_strength=0.5, noise=0.0, shuffle=True, coef=False, random_state=None)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.make_regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "X_all, y_all = datasets.make_regression(n_samples=50, n_features=50, n_informative=10) #, noise=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X_all, y_all, train_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25, 50), (25,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25, 50), (25,))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "model = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def sse(resid):\n",
    "    return sum(resid**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.8411047954908775e-25"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_train = y_train - model.predict(X_train)\n",
    "sse_train = sse(resid_train)\n",
    "sse_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.8411047954908775e-25"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_test = y_test - model.predict(X_test)\n",
    "sse_test = sse(resid_train)\n",
    "sse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.314074006752018"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_residuals_and_coeff(resid_train, resid_test, coeff):\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(12, 3))\n",
    "    axes[0].bar(np.arange(len(resid_train)), resid_train)\n",
    "    axes[0].set_xlabel(\"sample number\")\n",
    "    axes[0].set_ylabel(\"residual\")\n",
    "    axes[0].set_title(\"training data\")\n",
    "    axes[1].bar(np.arange(len(resid_test)), resid_test)\n",
    "    axes[1].set_xlabel(\"sample number\")\n",
    "    axes[1].set_ylabel(\"residual\")\n",
    "    axes[1].set_title(\"testing data\")\n",
    "    axes[2].bar(np.arange(len(coeff)), coeff)\n",
    "    axes[2].set_xlabel(\"coefficient number\")\n",
    "    axes[2].set_ylabel(\"coefficient\")\n",
    "    fig.tight_layout()\n",
    "    return fig, axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_residuals_and_coeff(resid_train, resid_test, model.coef_)\n",
    "fig.savefig(\"ch15-regression-ols.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "model = linear_model.Ridge() #alpha=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#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>Ridge()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178.50695164950955"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_train = y_train - model.predict(X_train)\n",
    "sse_train = sum(resid_train**2)\n",
    "sse_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212737.00160105838"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_test = y_test - model.predict(X_test)\n",
    "sse_test = sum(resid_test**2)\n",
    "sse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9994595515017335, 0.3167033273607547)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train), model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_residuals_and_coeff(resid_train, resid_test, model.coef_)\n",
    "fig.savefig(\"ch15-regression-ridge.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "model = linear_model.Lasso(alpha=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "309.7497138953243"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_train = y_train - model.predict(X_train)\n",
    "sse_train = sse(resid_train)\n",
    "sse_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1489.1176065002646"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_test = y_test - model.predict(X_test)\n",
    "sse_test = sse(resid_test)\n",
    "sse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_residuals_and_coeff(resid_train, resid_test, model.coef_)\n",
    "fig.savefig(\"ch15-regression-lasso.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "alphas = np.logspace(-4, 2, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.311e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.682e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 4.220e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.515e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.462e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.865e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 4.408e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.514e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.364e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.928e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.447e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.509e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.718e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 3.941e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: 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: 4.351e+01, tolerance: 3.303e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "coeffs = np.zeros((len(alphas), X_train.shape[1]))\n",
    "sse_train = np.zeros_like(alphas)\n",
    "sse_test = np.zeros_like(alphas)\n",
    "\n",
    "for n, alpha in enumerate(alphas):\n",
    "    model = linear_model.Lasso(alpha=alpha)\n",
    "    model.fit(X_train, y_train)\n",
    "    coeffs[n, :] = model.coef_\n",
    "    resid = y_train - model.predict(X_train)\n",
    "    sse_train[n] = sum(resid**2)\n",
    "    resid = y_test - model.predict(X_test)\n",
    "    sse_test[n] = sum(resid**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4), sharex=True)\n",
    "\n",
    "for n in range(coeffs.shape[1]):\n",
    "    axes[0].plot(np.log10(alphas), coeffs[:, n], color='k', lw=0.5)\n",
    "\n",
    "axes[1].semilogy(np.log10(alphas), sse_train, label=\"train\")\n",
    "axes[1].semilogy(np.log10(alphas), sse_test, label=\"test\")\n",
    "axes[1].legend(loc=0)\n",
    "\n",
    "axes[0].set_xlabel(r\"${\\log_{10}}\\alpha$\", fontsize=18)\n",
    "axes[0].set_ylabel(r\"coefficients\", fontsize=18)\n",
    "axes[1].set_xlabel(r\"${\\log_{10}}\\alpha$\", fontsize=18)\n",
    "axes[1].set_ylabel(r\"sse\", fontsize=18)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch15-regression-lasso-vs-alpha.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "model = linear_model.LassoCV()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LassoCV()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LassoCV</label><div class=\"sk-toggleable__content\"><pre>LassoCV()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LassoCV()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_all, y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06559238747534717"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5450589323146602"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_train = y_train - model.predict(X_train)\n",
    "sse_train = sse(resid_train)\n",
    "sse_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5321417406215792"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_test = y_test - model.predict(X_test)\n",
    "sse_test = sse(resid_test)\n",
    "sse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9999953221722068, 0.9999950788657098)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train), model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_residuals_and_coeff(resid_train, resid_test, model.coef_)\n",
    "fig.savefig(\"ch15-regression-lasso-cv.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = linear_model.ElasticNetCV()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ElasticNetCV()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNetCV</label><div class=\"sk-toggleable__content\"><pre>ElasticNetCV()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ElasticNetCV()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_all, y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13118477495069433"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.l1_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2183.83917293912"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_train = y_train - model.predict(X_train)\n",
    "sse_train = sum(resid_train**2)\n",
    "sse_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2650.0504463382536"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_test = y_test - model.predict(X_test)\n",
    "sse_test = sum(resid_test**2)\n",
    "sse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9933881981034111, 0.9914882195448783)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train), model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_residuals_and_coeff(resid_train, resid_test, model.coef_)\n",
    "fig.savefig(\"ch15-regression-elastic-net-cv.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils._bunch.Bunch"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# print(iris['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(\n",
    "    iris.data, iris.target, train_size=0.7, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "classifier = linear_model.LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_test_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        16\n",
      "           1       1.00      0.94      0.97        18\n",
      "           2       0.92      1.00      0.96        11\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.97      0.98      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16, 18, 11])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0],\n",
       "       [ 0, 17,  1],\n",
       "       [ 0,  0, 11]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0],\n",
       "       [ 0, 17,  1],\n",
       "       [ 0,  0, 11]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = tree.DecisionTreeClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test)\n",
    "metrics.confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0],\n",
       "       [ 0, 17,  1],\n",
       "       [ 0,  0, 11]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = neighbors.KNeighborsClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test)\n",
    "metrics.confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0],\n",
       "       [ 0, 17,  1],\n",
       "       [ 0,  0, 11]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = svm.SVC()\n",
    "classifier.fit(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test)\n",
    "metrics.confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0],\n",
       "       [ 0, 17,  1],\n",
       "       [ 0,  0, 11]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = ensemble.RandomForestClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test)\n",
    "metrics.confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "train_size_vec = np.linspace(0.1, 0.9, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "classifiers = [tree.DecisionTreeClassifier,\n",
    "               neighbors.KNeighborsClassifier,\n",
    "               svm.SVC,\n",
    "               ensemble.RandomForestClassifier\n",
    "              ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "cm_diags = np.zeros((3, len(train_size_vec), len(classifiers)), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "for n, train_size in enumerate(train_size_vec):\n",
    "    X_train, X_test, y_train, y_test = \\\n",
    "        model_selection.train_test_split(iris.data, iris.target, train_size=train_size)\n",
    "\n",
    "    for m, Classifier in enumerate(classifiers): \n",
    "        classifier = Classifier()\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_test_pred = classifier.predict(X_test)\n",
    "        cm_diags[:, n, m] = metrics.confusion_matrix(y_test, y_test_pred).diagonal()\n",
    "        cm_diags[:, n, m] /= np.bincount(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, len(classifiers), figsize=(12, 3))\n",
    "\n",
    "for m, Classifier in enumerate(classifiers): \n",
    "    axes[m].plot(train_size_vec, cm_diags[2, :, m], label=iris.target_names[2])\n",
    "    axes[m].plot(train_size_vec, cm_diags[1, :, m], label=iris.target_names[1])\n",
    "    axes[m].plot(train_size_vec, cm_diags[0, :, m], label=iris.target_names[0])\n",
    "    axes[m].set_title(type(Classifier()).__name__)\n",
    "    axes[m].set_ylim(0, 1.1)\n",
    "    axes[m].set_xlim(0.1, 0.9)\n",
    "    axes[m].set_ylabel(\"classification accuracy\")\n",
    "    axes[m].set_xlabel(\"training size ratio\")\n",
    "    axes[m].legend(loc=4)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch15-classification-comparison.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = iris.data, iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "n_clusters = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "c = cluster.KMeans(n_clusters=n_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/npbook_py310/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
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      ],
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "y_pred = c.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[::8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[::8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "idx_0, idx_1, idx_2 = (np.where(y_pred == n) for n in range(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred[idx_0], y_pred[idx_1], y_pred[idx_2] = 2, 0, 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2],\n",
       "      dtype=int32)"
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     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[::8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  0,  0],\n",
       "       [ 0, 48,  2],\n",
       "       [ 0, 14, 36]])"
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     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = X.shape[1]\n",
    "\n",
    "fig, axes = plt.subplots(N, N, figsize=(12, 12), sharex=True, sharey=True)\n",
    "\n",
    "colors = [\"coral\", \"blue\", \"green\"]\n",
    "markers = [\"^\", \"v\", \"o\"]\n",
    "for m in range(N):\n",
    "    for n in range(N):\n",
    "        for p in range(n_clusters):\n",
    "            mask = y_pred == p\n",
    "            axes[m, n].scatter(X[:, m][mask], X[:, n][mask],\n",
    "                               marker=markers[p], s=30, \n",
    "                               color=colors[p], alpha=0.25)\n",
    "\n",
    "        for idx in np.where(y != y_pred):\n",
    "            axes[m, n].scatter(X[idx, m], X[idx, n],\n",
    "                               marker=\"s\", s=30, \n",
    "                               edgecolor=\"red\", \n",
    "                               facecolor=(1,1,1,0))\n",
    "            \n",
    "            \n",
    "    axes[N-1, m].set_xlabel(iris.feature_names[m], fontsize=16)\n",
    "    axes[m, 0].set_ylabel(iris.feature_names[m], fontsize=16)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch15-clustering.pdf\")\n",
    "fig.savefig(\"ch15-clustering.png\", dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%reload_ext version_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "3.10.12 64bit [Clang 14.0.6 ]"
        },
        {
         "module": "IPython",
         "version": "8.12.0"
        },
        {
         "module": "OS",
         "version": "macOS 10.15.7 x86_64 i386 64bit"
        },
        {
         "module": "sklearn",
         "version": "1.3.0"
        },
        {
         "module": "numpy",
         "version": "1.22.3"
        },
        {
         "module": "matplotlib",
         "version": "3.7.1"
        },
        {
         "module": "seaborn",
         "version": "0.12.2"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.10.12 64bit [Clang 14.0.6 ]</td></tr><tr><td>IPython</td><td>8.12.0</td></tr><tr><td>OS</td><td>macOS 10.15.7 x86\\_64 i386 64bit</td></tr><tr><td>sklearn</td><td>1.3.0</td></tr><tr><td>numpy</td><td>1.22.3</td></tr><tr><td>matplotlib</td><td>3.7.1</td></tr><tr><td>seaborn</td><td>0.12.2</td></tr><tr><td colspan='2'>Sun Nov 03 09:20:45 2024 JST</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.10.12 64bit [Clang 14.0.6 ] \\\\ \\hline\n",
       "IPython & 8.12.0 \\\\ \\hline\n",
       "OS & macOS 10.15.7 x86\\_64 i386 64bit \\\\ \\hline\n",
       "sklearn & 1.3.0 \\\\ \\hline\n",
       "numpy & 1.22.3 \\\\ \\hline\n",
       "matplotlib & 3.7.1 \\\\ \\hline\n",
       "seaborn & 0.12.2 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Sun Nov 03 09:20:45 2024 JST} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 3.10.12 64bit [Clang 14.0.6 ]\n",
       "IPython 8.12.0\n",
       "OS macOS 10.15.7 x86_64 i386 64bit\n",
       "sklearn 1.3.0\n",
       "numpy 1.22.3\n",
       "matplotlib 3.7.1\n",
       "seaborn 0.12.2\n",
       "Sun Nov 03 09:20:45 2024 JST"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%version_information sklearn, numpy, matplotlib, seaborn"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "npbook_py310",
   "language": "python",
   "name": "npbook_py310"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}