Z07_A_cross_validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Regresja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rozważmy zbiór Boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
    "raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
    "data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
    "target = raw_df.values[1::2, 2]\n",
    "feature_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the data\n",
    "boston_X = data\n",
    "boston_Y = target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podzielmy zbiór na część testową i treningową ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training/testing sets\n",
    "boston_X_train = boston_X[:-50]\n",
    "boston_X_test = boston_X[-50:]\n",
    " \n",
    "# Split the targets into training/testing sets\n",
    "boston_y_train = boston_Y[:-50]\n",
    "boston_y_test = boston_Y[-50:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=boston_X_train\n",
    "y=boston_y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zadanie\n",
    "Znajdż najleprzy model dzieląc na zbiór testowy i terningowy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import linear_model\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn import model_selection\n",
    "\n",
    "seed=123\n",
    "kfold = model_selection.KFold(n_splits=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.421e+03, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.415e+03, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.380e+03, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.316e+03, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 7.027e+02, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 6.618e+02, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 6.317e+02, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 6.481e+02, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 6.901e+02, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 3.817e+02, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 3.504e+02, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 3.238e+02, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 3.349e+02, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 3.717e+02, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 2.082e+02, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.680e+03, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.674e+03, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.603e+03, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.479e+03, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 8.161e+02, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 7.575e+02, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 7.332e+02, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 7.408e+02, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 7.429e+02, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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+02, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 4.023e+02, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 3.671e+02, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 3.753e+02, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 4.198e+02, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 2.333e+02, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 2.026e+03, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 2.048e+03, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.881e+03, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.863e+03, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.099e+03, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 8.990e+02, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 8.552e+02, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 8.747e+02, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 8.510e+02, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 4.999e+02, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 4.579e+02, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 4.183e+02, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 4.378e+02, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 4.844e+02, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 2.666e+02, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 2.576e+03, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 2.590e+03, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 2.227e+03, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 2.043e+03, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.177e+03, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.105e+03, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.059e+03, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.091e+03, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 1.042e+03, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 6.067e+02, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 5.660e+02, tolerance: 3.828e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 5.129e+02, tolerance: 3.406e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 5.256e+02, tolerance: 2.666e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 5.663e+02, tolerance: 3.673e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:645: 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: 3.297e+02, tolerance: 2.539e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'elasticnet__alpha': 1, 'polynomialfeatures__degree': 1}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_1 = GridSearchCV(make_pipeline(PolynomialFeatures(degree=2), ElasticNet(alpha=1, random_state=seed)),\n",
    "                    param_grid={'polynomialfeatures__degree': [1, 2, 3, 4],\n",
    "                    'elasticnet__alpha': [0.01, 0.1, 1, 10]},\n",
    "                    cv=kfold,\n",
    "                    refit=True)\n",
    "grid_1.fit(X, y)\n",
    "grid_1.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.56359058e-01, -1.81971026e+02, -1.25827594e+03,\n",
       "        -2.94601984e+04],\n",
       "       [ 4.14883696e-01, -9.67566969e-01, -5.57237890e+02,\n",
       "        -3.23763009e+05],\n",
       "       [ 4.70838951e-01,  1.12055989e-01, -8.87555304e+01,\n",
       "        -8.78702543e+03],\n",
       "       [ 2.14677917e-01,  1.92715153e-01, -9.81211483e-01,\n",
       "        -1.08972348e+03]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_1.cv_results_['mean_test_score'].reshape(4, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(grid_1.cv_results_['mean_test_score'].reshape(4, -1),\n",
    "vmin=0, cmap=\"viridis\")\n",
    "plt.xlabel(\"elasticnet__alpha\")\n",
    "plt.ylabel(\"polynomialfeatures__degree\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet\n",
      "R^2: 0.2620978059892657\n",
      "Explained variance score: 0.26252032482831233\n",
      "Median absolute error: 2.037273249852337\n",
      "Mean squared error: 13.918975295405083\n",
      "Mean absolute errors: 2.7860251774001723\n"
     ]
    }
   ],
   "source": [
    "from sklearn import  metrics\n",
    "\n",
    "X_test=boston_X_test\n",
    "y_test=boston_y_test\n",
    "\n",
    "models = []\n",
    "models.append(('ElasticNet', grid_1.best_estimator_))\n",
    "# models.append(('Lasso', grid_2.best_estimator_))\n",
    "# models.append(('Ridge', grid_3.best_estimator_))\n",
    "# models.append(('LR', grid_4.best_estimator_))\n",
    "\n",
    "r2 = []\n",
    "explained_variance_score = []\n",
    "median_absolute_error = []\n",
    "mean_squared_error = []\n",
    "mean_absolute_error = []\n",
    "for name, model in models:\n",
    "    print(name)\n",
    "    print(\"R^2: {}\".format(metrics.r2_score(y_test, model.predict(X_test)) ))\n",
    "    print(\"Explained variance score: {}\".format( metrics.explained_variance_score(y_test, model.predict(X_test)) ))\n",
    "    print(\"Median absolute error: {}\".format( metrics.median_absolute_error(y_test, model.predict(X_test)) ))\n",
    "    print(\"Mean squared error: {}\".format( metrics.mean_squared_error(y_test, model.predict(X_test)) ))\n",
    "    print(\"Mean absolute errors: {}\".format(metrics.mean_absolute_error(y_test, model.predict(X_test)) ))\n",
    "    r2.append(metrics.r2_score(y_test, model.predict(X_test)))\n",
    "    explained_variance_score.append(metrics.explained_variance_score(y_test, model.predict(X_test)))\n",
    "    median_absolute_error.append( metrics.median_absolute_error(y_test, model.predict(X_test)))\n",
    "    mean_squared_error.append(metrics.mean_squared_error(y_test, model.predict(X_test)))\n",
    "    mean_absolute_error.append(metrics.mean_absolute_error(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>r2</th>\n",
       "      <th>explained_variance_score</th>\n",
       "      <th>median_absolute_error</th>\n",
       "      <th>mean_squared_error</th>\n",
       "      <th>mean_absolute_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>0.262098</td>\n",
       "      <td>0.26252</td>\n",
       "      <td>2.037273</td>\n",
       "      <td>13.918975</td>\n",
       "      <td>2.786025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Method        r2  explained_variance_score  median_absolute_error  \\\n",
       "0  ElasticNet  0.262098                   0.26252               2.037273   \n",
       "\n",
       "   mean_squared_error  mean_absolute_error  \n",
       "0           13.918975             2.786025  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d = {'r2': r2, \n",
    "     'explained_variance_score': explained_variance_score, \n",
    "     'median_absolute_error': median_absolute_error,\n",
    "     'mean_squared_error' : mean_squared_error,\n",
    "     'mean_absolute_error' : mean_absolute_error,\n",
    "    }\n",
    "df = pd.DataFrame(data=d)\n",
    "df.insert(loc=0, column='Method', value=['ElasticNet'])#,'Lasso','Ridge','LR'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}