ch15-machine-learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning\n",
    "- __Toy datasets__\n",
    "- __Regression__\n",
    "- __Classification__\n",
    "- __Clustering__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn import model_selection # was cross_validation\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn import neighbors\n",
    "from sklearn import svm\n",
    "from sklearn import ensemble\n",
    "from sklearn import cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create regression problem with 50 samples & 50 features\n",
    "# only 10 features contribute to model's predictive power\n",
    "\n",
    "X_all, y_all = datasets.make_regression(\n",
    "    n_samples=50, \n",
    "    n_features=50, \n",
    "    n_informative=10) #, noise=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(\n",
    "    X_all, y_all, train_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25, 50), (25,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25, 50), (25,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sse(resid):\n",
    "    return sum(resid**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.822643241858254e-24"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training residuals basically zero (expected) due to overfitting (2X features vs data points)\n",
    "resid_train = y_train - model.predict(X_train)\n",
    "sse_train = sse(resid_train)\n",
    "sse_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.822643241858254e-24"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6704734083128553"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# big diff between training score & test score\n",
    "# indicates probable OVERFIT going on.\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "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": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 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": "markdown",
   "metadata": {},
   "source": [
    "### Regularized regression\n",
    "- Not always practical to gather enough samples to overcome overfit\n",
    "- Ordinary LE: params chosen to minimize sum of squared residuals\n",
    "- Regularization = adding a penalty in the objective function\n",
    "- Two most popular: adding L1 or L2 norms of parameter vector to minimizatio objective function. They are known as __Lasso__ and __Ridge__ regression respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = linear_model.Ridge(alpha=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=2.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "      normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3198.687325364639, 253651.40665117663)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find model predictions & corresponding SSE values for training & test datasets\n",
    "resid_train = y_train - model.predict(X_train)\n",
    "sse_train = sum(resid_train**2)\n",
    "\n",
    "resid_test = y_test - model.predict(X_test)\n",
    "sse_test = sum(resid_test**2)\n",
    "\n",
    "sse_train, sse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 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": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = linear_model.Lasso(alpha=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "      normalize=False, positive=False, precompute=False, random_state=None,\n",
       "      selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(355.2631197078741, 925.1210329073904)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_train = y_train - model.predict(X_train)\n",
    "sse_train = sse(resid_train)\n",
    "\n",
    "resid_test = y_test - model.predict(X_test)\n",
    "sse_test = sse(resid_test)\n",
    "\n",
    "sse_train, sse_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Note: training SSE decreased by ~10X; testing SSE decreased by >250X.\n",
    "* Lasso should be better at predicting the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 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": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# alpha was chosen arbitrarily; best value is problem-dependent.\n",
    "# let's analyze fit vs alpha.\n",
    "alphas = np.logspace(-4, 2, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 107.84767754056492, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120.79235662528833, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 137.44280341239332, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 152.4458209980043, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 149.93233560985973, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 162.54538319023573, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 184.348047572921, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202.27045451832072, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219.76156836710558, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.97985802831477, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273.68330319425377, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.823121994061, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 210.2107734000126, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 157.28619375478908, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 196.76260068274726, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 215.96201869961078, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 179.59561189216285, tolerance: 101.02958727548564\n",
      "  positive)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 141.13616467909262, tolerance: 101.02958727548564\n",
      "  positive)\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",
    "    \n",
    "    resid = y_train - model.predict(X_train)\n",
    "    sse_train[n] = sum(resid**2)\n",
    "    \n",
    "    resid = y_test - model.predict(X_test)\n",
    "    sse_test[n] = sum(resid**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot coefficients (left) and SSE (right) for training & test datasets\n",
    "# for LASSO regression\n",
    "# vs logarithm of alpha regularization parameter.\n",
    "\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validated regression\n",
    "* Use RidgeCV and LassoCV to automatically find optimal values of alpha.\n",
    "* CV = \"cross validation\"\n",
    "* Default CV setup: k-fold with k=3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=None, copy_X=True, cv='warn', eps=0.001, fit_intercept=True,\n",
       "        max_iter=1000, n_alphas=100, n_jobs=None, normalize=False,\n",
       "        positive=False, precompute='auto', random_state=None,\n",
       "        selection='cyclic', tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.LassoCV()\n",
    "model.fit(X_all, y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10636631704955543"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alpha selected by CV process.\n",
    "model.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.724131950403188, 2.879748529204052)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_train = y_train - model.predict(X_train)\n",
    "sse_train = sse(resid_train)\n",
    "\n",
    "resid_test = y_test - model.predict(X_test)\n",
    "sse_test = sse(resid_test)\n",
    "\n",
    "sse_train, sse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9999953240114329, 0.9999963935416253)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train), model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xSvFALEieSbICeDdwAHANcF6SdVV1aU+xI4GbquqhSQ4B3gw8d777lpabEcyFD6mq5yZ5HkBV3ZIkww5Ko6n3/FnC58yy0m9Fan2SrYH3AecDPwe+tmBRSVqOFirP7A1cXlVXACQ5FTgI6K1IHQS8oZ0+A3hXklRVDWD/I88BLzTCbktyb5oBJkjyEHpG75O0vPVVkaqqF7eTJyb5DHC/qrpo4cKStNiGfSV5AfPMjsDVPfPXAI+ZqkxVbUryE2Bb4IbeQknWAGsAVq1a1dfOZzquM1VCZjr+/Xw/c41hfP18Y5jpM853/SBiXOjP0M/3tNAxLvT2+9nGEqxYvx74DLBzko8C+wJHDDWiOZj43S3B70HqpH4Hm3jiZMuq6suDD0nScrQU8kxVrQXWAoyNjQ2ktcp/aKTuqqpzklwA7AMEeGlV3TDD2yQtE/127et9fsK9aLrKnI8PypQ0OAuVZ67lrvda7dQum6zMNUk2pxnk4sZ57lfSEpVkt6r6bpK92kUb2tdVSVZV1QXDik1Sd/Tbte+PeueT7Ay8fUEikrQsLWCeOQ/YNckuNBWmQ4DnTyizjubBm1+jGT3wC94fJS1rL6fpxnvCJOsKLyRLYhYP5J3gGuDhgwxEkiYYSJ5p73k6BvgssAL4YFVdkuSNwPqqWgd8ADglyeXAj2kqW5KWqapa077+wbBjkdRd/d4j9U7aEWtonj21J2CztqSBWcg8U1Vn0zzkt3fZ63qmfwU8exD7kjQ6khwNfLSqbm7nHwA8r6r+abiRSeqCvoc/75neBHysqr6yAPFIdzPs0eS0aMwzkrrmf1TVu8dnquqmJP8DsCIlqe97pE5e6EAkLW/mGUkdtKL3mXLtA763GHJMkjpi2opUkou5s6vN3VTVIwcekaRlxTwjqcM+A5yW5L3t/J+3y2bUVrrWA9dW1TPaAW9OpXlG3fnAYVV12wLELGmRzNQi9Yz29ej29ZT29QVM84+PJM2CeUZSV72apvL0F+38OcD7+3zvS4HLgPu1828G3lZVpyY5ETgSeM8AY5W0yKatSFXVVQBJDqiqR/WsenX7gLpjFzI4SaPPPCOpq6rqDprKzqwqPEl2Ap4OvAl4eZLQDJk+/uiFk4E3zHa7krplsz7LJcm+PTOPm8V7Jakf5hlJnZDk4+3rxUkumvjTxybeDvw1cEc7vy1wc1VtauevAXacYt9rkqxPsn7jxo3z/CSSFlK/o/YdCXwwyf2BADcB/33BolJnTDViHozOqHnL4TMuEeYZSV3xsvb1GdOWmkSSZwDXV9X5Sfab7furai2wFmBsbMzuzVKH9Ttq3/nAHu0/OFTVTxY0KknLzqjmGSvj0pJ0JrAXcFxVHTbL9+4LPDPJ04B70dwj9Q5g6ySbt61SOwHXDjJgSYtvplH7XlBVH0ny8gnLAaiqty5gbAvO5xNJwzfqeUbSkrRFkucDj0vyJxNXVtUnp3pjVb0GeA1A2yL1yqo6NMnpwME0I/cdDnxqIQKXtHhmapG6T/u61UIHImnZMs9I6poXAYcCWwN/NGFdAVNWpKbxauDUJMcB3wI+MK8IJQ3dTKP2vbd9/dvFCUfLkS2Dy5t5RlIH7VBVf5HkW+09S3NSVV8CvtROXwHsPZjwJHVBXyNiJXlLkvsluUeSc5NsTPKChQ5O0vJhnpHUIa9pX1801CgkdVq/Qwv/YVX9lGb0miuBhwKvWqigJC1L5hlJXXFjks8BuyRZN/Fn2MFJ6oZ+hz8fL/d04PSq+sn4jeDzkeRAmpFsVgDvr6rjJ6y/J/Bh4NHAjcBzq+rKee9Yv2G3OnXIguQZSZqDp9OM2ncKcMKQY5HUUf1WpM5M8l3gl8BfJFkJ/Go+O06yAng3cADNg+nOS7Kuqi7tKXYkcFNVPTTJIcCbgefOZ7+SOmsh8sw2wGnAappWrudU1U0TyuwJvIdmiOLbgTdV1Wnz2a9GjxeXlpequg34epLHVdXGJFtW1S3DjktSt/TVta+qjgUeB4xV1a+BW4CD5rnvvYHLq+qKNmGdOsk2DwJObqfPAJ4cL1FLI2mB8syxwLlVtStwbjs/0S3An1XV7wIHAm9PsvU89ytpNDw0yaXAdwGS7JHkn4Yck6SO6KtFKsmWwIuBVcAa4IHAw2geWDdXOwJX98xfAzxmqjJVtSnJT4BtgRsmxLemjYtVq1b1HcBMVxhnWj9Tt7ip1o+Xmev2+9lHv+sX6hiMv7efz9D176GfK9Hz3cZ8j/MgfpeGfcV9gfLMQcB+7fTJNKNnvbq3QFV9v2f6v5JcD6wEbp7HfiWNhrcDTwHWAVTVt5M8cbghSeqKfgeb+BBwG83VYmiexn3cgkQ0B1W1tqrGqmps5cqVww5H0twsRJ7Zvqo2tNPXAdtPVzjJ3sAWwH9MsX5NkvVJ1m/cuHGeoUlaCqrq6gmLbh9KIJI6p9+K1EOq6i3ArwHafsLz7WJ3LbBzz/xO7bJJyyTZHLg/zaATkkbPnPJMks8n+c4kP3fpFlhVRfMgzam2swPNjeUvrKo7JivjRRtp2bk6yeOAah/N8ErgsmEHJakb+h1s4rYk96b9JyTJQ4Bb57nv84Bdk+xCU2E6BHj+hDLrgMOBrwEHA19o/xmSNHrmlGeqav+p1iX5UZIdqmpDW1G6fopy9wPOAl5bVV+fU/SSRtGLaEYX3hH4L+CzwNFDjUhSZ8xYkWoHdzgR+Aywc5KPAvsCR8xnx+09T8fQJKUVwAer6pIkbwTWV9U64APAKUkuB35MU9mSNGIWKs9w58WY49vXT02y7y2AfwY+XFVnzHN/kkZIVd0AHDrsOCR104wVqaqqJK+iuWF7H5quNi9tk8u8VNXZwNkTlr2uZ/pXwLPnux9J3baAeeZ44ONJjgSuAp4DkGQMeFFVHdUueyKwbZIj2vcdUVUXznPfkpa4JDsB76S5sAPwbzS56ZrhRSWpK/rt2ncB8OCqmnroL0man4Hnmaq6EXjyJMvXA0e10x8BPjKofUoaKR8C/i93XtR9QbvsgKFFJKkz+q1IPQY4NMlVwC9orhZXVT1ywSKTtNyYZyR1zcqq+lDP/ElJXja0aCR1Sr8VqacsaBSSZJ6R1D03JnkB8LF2/nk4erCkVl8Vqaq6aqEDkbS8mWckddB/p7lH6m00I4p+lfkPgiNpRPTbIiVJkrTcvBE4vKpuAkiyDfAPNBUsScucFSnNy5XHP33YIUiStFAeOV6JAqiqHyd51DADktQdmw07AEmSpI7aLMkDxmfaFikvQksCTAadZmuPJElDdQLwtSSnt/PPBt40xHgkdYgVKUmSpElU1YeTrAee1C76k6q6dJgxSeoOK1KSJElTaCtOVp4k3Y33SEmSJEnSLNkiJUmStIytPvasu8x7j7bUHytSkqRO8586LSVJdgY+DGxP8xDftVX1jnbEv9OA1cCVwHN6h1aXtPTYtU+SJGlwNgGvqKrdgX2Ao5PsDhwLnFtVuwLntvOSljArUpIkSQNSVRuq6oJ2+mfAZcCOwEHAyW2xk4FnDSdCSYNi1z6NPLsFLV+z6UqT5H40I3P9S1Uds1gxynNUoyvJauBRwDeA7atqQ7vqOpquf5KWMFukJI2y2XSl+Tvgy4sSlaSRl+S+wCeAl1XVT3vXVVXR3D812fvWJFmfZP3GjRsXIVJJc2WLlKRRdhCwXzt9MvAl4NUTCyV5NM3V4c8AY4sUm7Sk2HLYvyT3oKlEfbSqPtku/lGSHapqQ5IdgOsne29VrQXWAoyNjU1a2ZLUDbZISRplM3alSbIZcALwypk25pViSTNJEuADwGVV9daeVeuAw9vpw4FPLXZskgbLFilJS1qSzwO/Pcmq1/bOVFUlmezq7ouBs6vqmub/n6l5pVhSH/YFDgMuTnJhu+xvgOOBjyc5ErgKeM6Q4pM0IFakRpxdMUaD3+PUqmr/qdYl6acrzWOBJyR5MXBfYIskP68qhyaWNGtV9e/AVFdlnryYsUhaWFaktORZydA0xrvSHM8UXWmq6tDx6SRHAGNWoiRJ0ky8R0rSKDseOCDJD4D923mSjCV5/1AjkyRJS5otUpJGVlXdyCRdaapqPXDUJMtPAk5a8MAkSdKSZ0VKC8pud9LweR5KkjR4VqQkSQvKipwkaRR5j5QkSZIkzZItUvPgVVZJUlf4N0mSFpcVKUmSJP3G6mPPusu8lXRpcnbtkyRJkqRZsiIlSZIkSbM0lIpUkm2SnJPkB+3rA6Yod3uSC9ufdYsdpyRJkiRNZlgtUscC51bVrsC57fxkfllVe7Y/z1y88CRJkiRpasOqSB0EnNxOnww8a0hxSJIkSdKsDWvUvu2rakM7fR2w/RTl7pVkPbAJOL6q/mWyQknWAGsAVq1aNehYO82RdCRJkqTFt2AVqSSfB357klWv7Z2pqkpSU2zmQVV1bZIHA19IcnFV/cfEQlW1FlgLMDY2NtW2JEmSJGkgFqwiVVX7T7UuyY+S7FBVG5LsAFw/xTaubV+vSPIl4FHA3SpSkqZny6UkSdJgDatr3zrgcOD49vVTEwu0I/ndUlW3JtkO2Bd4y6JGKUlSH7xYIalrfLDywhvWYBPHAwck+QGwfztPkrEk72/LPBxYn+TbwBdp7pG6dCjRSlqSZvGohVVJPpfksiSXJlm9uJFKkqSlZigtUlV1I/DkSZavB45qp78K/LdFDk3SaBl/1MLxSY5t5189SbkPA2+qqnOS3Be4YzGDlCRJS8+wWqQkaTHM+KiFJLsDm1fVOQBV9fOqumXxQpQkSUuRFSlJo6yfRy38DnBzkk8m+VaSv0+yYrKNJVmTZH2S9Rs3blyomCVJ0hIwrMEmJGkgBvCohc2BJ9CMCvpD4DTgCOADEwv6qAVJkjTOipSkJW0Aj1q4Briwqq5o3/MvwD5MUpGSJEkaZ9c+SaNs/FELMMWjFoDzgK2TrGznnwQ4QqgkSZqWFSlJo2zGRy1U1e3AK4Fzk1wMBHjfkOKVNMKSHJjke0kub0cSlbSE2bVP0sjq51EL7fw5wCMXMTRJy0w7iM27gQNouhSfl2Sdz8iUli5bpCRJkhbe3sDlVXVFVd0GnErziAZJS1SqRmvgqbGxsVq/fv2ww5CWnSTnV9XYsONYLOYaafEt5TyT5GDgwKo6qp0/DHhMVR0zodwaYA3AqlWrHn3VVVcteqzSctdvrrFFSpIkqSOqam1VjVXV2MqVK2d+g6ShsSIlSZK08K4Fdu6Z36ldJmmJsiIlSZK08M4Ddk2yS5ItgENoHtEgaYly1D5JkqQFVlWbkhwDfBZYAXywqi4ZcliS5sGKlCRJ0iKoqrOBs4cdh6TBsGufJEmSJM3SyA1/nmQjMJexQrcDbhhwOIPW9Ri7Hh8Y46BMFuODqmrZDDE1x1yzVL/brjHG+et6fGCemUue6fr32vX4wBgHoevxwcwx9pVrRq4iNVdJ1nf92RRdj7Hr8YExDspSiLGLlsJxM8bB6HqMXY8PlkaMXdP1Y9b1+MAYB6Hr8cHgYrRrnyRJkiTNkhUpSZIkSZolK1J3WjvsAPrQ9Ri7Hh8Y46AshRi7aCkcN2McjK7H2PX4YGnE2DVdP2Zdjw+McRC6Hh8MKEbvkZIkSZKkWbJFSpIkSZJmyYqUJEmSJM3Ssq9IJTkwyfeSXJ7k2GHHM5kkVya5OMmFSdYPOx6AJB9Mcn2S7/Qs2ybJOUl+0L4+oIMxviHJte2xvDDJ04Yc485Jvpjk0iSXJHlpu7wTx3Ka+Dp1HJcCc83cdD3XmGcWPMZOHcuu6mJ+6fq528bT6XMjyb2SfDPJt9v4/rZdvkuSb7Tf92lJthhGfBNiXZHkW0nO7GKMk/19G8T3vKwrUklWAO8GngrsDjwvye7DjWpKf1BVe3ZoXP6TgAMnLDsWOLeqdgXObeeH6STuHiPA29pjuWdVnb3IMU20CXhFVe0O7AMc3f4OduVYThUfdOs4dpq5Zl5Ootu55iTMMwsZI3TrWHZOh/PLSXT73IXunxu3Ak+qqj2APYEDk+wDvJnmvHgocBNw5JDi6/VS4LKe+S7GOPHv27y/52VdkQL2Bi6vqiuq6jbgVOCgIce0JFTVl4EfT1h8EHByO30y8KxFDWqCKWLslKraUFUXtNM/o0lCO9KRYzlWRwVvAAAJOElEQVRNfJodc80cdT3XmGcGw1wzL53ML10/d6H750Y1ft7O3qP9KeBJwBnt8qEfxyQ7AU8H3t/Oh47FOIV5f8/LvSK1I3B1z/w1dDNxF/C5JOcnWTPsYKaxfVVtaKevA7YfZjDTOCbJRW23g6F2K+iVZDXwKOAbdPBYTogPOnocO8pcM1idOz8m0cnzo+t5Bsw1c7BU8gt09HcOuntutF3mLgSuB84B/gO4uao2tUW68H2/Hfhr4I52flu6F+Nkf9/m/T0v94rUUvH4qtqLptn+6CRPHHZAM6lmXP0ujq3/HuAhNE3kG4AThhtOI8l9gU8AL6uqn/au68KxnCS+Th5HzZu5ZjA6eX50Pc+AuWY56crvHHT73Kiq26tqT2AnmtbH3YYVy2SSPAO4vqrOH3YsM5j279tcv+flXpG6Fti5Z36ndlmnVNW17ev1wD/TnEhd9KMkOwC0r9cPOZ67qaoftUnpDuB9dOBYJrkHTQL/aFV9sl3cmWM5WXxdPI4dZ64ZrM6cH5Pp4vnR9TzTxmCumZslkV9anfqda+Po/LkBUFU3A18EHgtsnWTzdtWwv+99gWcmuZKmW+mTgHfQrRin+vs27+95uVekzgN2bUcW2QI4BFg35JjuIsl9kmw1Pg38IfCd6d81NOuAw9vpw4FPDTGWSY2fMK0/ZsjHsu1H/AHgsqp6a8+qThzLqeLr2nFcAsw1g9WJ82MqXTs/up5nwFwzT53PLz068zsH3T83kqxMsnU7fW/gAJr7uL4IHDzs+ACq6jVVtVNVrab53ftCVR1Kh2Kc5u/b/L/nqlrWP8DTgO/T9Dl97bDjmSS+BwPfbn8u6UqMwMdouln8mqbv65E0fWLPBX4AfB7YpoMxngJcDFzUnkA7DDnGx9M0JV8EXNj+PK0rx3Ka+Dp1HJfCj7lmznF1OteYZxY8xk4dy67+dDG/dP3cbWPs9LkBPBL4Vhvfd4DXtcsfDHwTuBw4HbjnsL/vNq79gDO7FuNUf98G8T2n3ZAkSZIkqU/LvWufJEmSJM2aFSlJkiRJmiUrUpIkSZI0S1akJEmSJGmWrEhJkiRJ0ixZkdKCS/KlJGMdiOOIJO8adhySBs88I2kqSe6Z5PNJLkzy3CRPSHJJO79jkjNmeP/7k+w+x33vl+Rxc4t8bpKsTuIz1xbB5jMXkQSQZEVV3T7sOCSNLvOMtCAeBVBVewIkORH4P1X1kXb9wVO9sX3fUfPY937Az4GvzmMbiyrJ5lW1adhxLAW2SC1D7ROez0ry7STfSfLcdvnrkpzXLlvbPvF7/Erv25KsT3JZkt9L8skkP0hyXFtmdZLvJvloW+aMJFtOsu8/TPK1JBckOT3JfScp86Ukb07yzSTfT/KEdvldrvQmOTPJfu30z5P8fXuF6fNJ9m63c0WSZ/Zsfud2+Q+SvL5nWy9o93dhkvcmWdGz3ROSfBt47AAOv7QsmGfMM9IgJPmzJBe1ueSUdtnqJF9ol5+bZFW7fGWST7Q55rwk+yb5LeAjwO+1596fA88B/q7NJb9pvUmyIsk/tPnpoiR/2S7/TYv3VPklyZVJ/rZdfnGS3ZKsBl4E/FW77ydM+GxvSPLBnjzykp7P952ecq9M8oaeWKbNla3NJ8uVSR6d5F+TnJ/ks0l26Nnu25OsB146wK9wpFmRWp4OBP6rqvaoqkcAn2mXv6uqfq9ddm/gGT3vua2qxoATgU8BRwOPAI5Ism1b5mHAP1XVw4GfAi/u3WmS7YD/CexfVXsB64GXTxHj5lW1N/Ay4PVTlOl1H+ALVfW7wM+A44ADgD8G3thTbm/gT2meFv7sJGNJHg48F9i3vVp1O3Boz3a/0R6rf+8jDkkN84x5RpqXJL9Lcz4/qar24M5/8N8JnFxVjwQ+Cvxju/wdwNuq6vdozsH3V9X1wFHAv1XVnlX1XmAd8KqqOpS7WgOsBvbs2XZvPDPllxva5e8BXllVV9Lks7e1+/63ST7mbsBTaPLG65Pco49DM6dc2W77ncDBVfVo4IPAm3q2u0VVjVXVCX3EIOzat1xdDJyQ5M3AmT0n9h8k+WtgS2Ab4BLg0+26dT3vvaSqNgAkuQLYGbgZuLqqvtKW+wjwEuAfeva7D7A78JU0F6G3AL42RYyfbF/Pp0lqM7mNO/9Ruxi4tap+neTiCe8/p6pubGP/JPB4YBPwaOC8Nq57A9e35W8HPtHH/iXdlXkG84w0T08CTq+qGwCq6sft8scCf9JOnwK8pZ3eH9i9PccA7pdJWqSnsT9w4ni3tp79jZspv/TmlD+hP2dV1a3ArUmuB7bv4z1zzZWfoalwndPGvwLY0LPd0/qMWS0rUstQVX0/yV7A04DjkpxLk4T+CRirqqvbJuR79bzt1vb1jp7p8fnx36OauKsJ86H5B+N5fYQ5vo/be7a/ibu2ovbG9+uqGt/fb2KsqjuS9P6eTxZjaK5svWaSOH7l/QrS7Jln7hajeUZaeJsB+1TVr3oX9lSs5mum/DJZTplJb64bf990eaj3PbPNlaGpeE3VhfgXfcasll37lqEkDwRuaW+y/HtgL+48SW9or95Me+PlFFYlGT85nw9M7KLydWDfJA9t47hPkt+ZxfavBPZMslmSnWmawWfrgCTbJLk38CzgK8C5wMFtP2ra9Q+aw7Yltcwz5hlpAL5A0z12W2jOm3b5V4FD2ulDgfEW788Bfzn+5iR7znJ/5wB/Pn5hpGd/4+aSX34GbDXLOH4E/FaSbZPck7t2ge7XZLnye8DK8eVJ7tF2n9QcWZFanv4b8M0kF9LcF3BcVd0MvA/4DvBZ4Lw5bPd7wNFJLgMeQNNH+DeqaiNwBPCxJBfRNIfvNovtfwX4T+BSmv7QF8whxm/SdKG5CPhEVa2vqktp+jx/ro3rHGCHOWxb0p3MM+YZaV6q6hKae3j+Nc1gLG9tV/0l8ML2XDqMO++degkwlmagiEtpBnqYjfcDPwQuavf3/AnxzCW/fBr440wy2MRUqurXNPddfpMmV3x3Nh+idbdcWVW30VzAenP7+S4EFnVo9lGTO3spSHOXZmSaM9sbyCVp4MwzkqQusUVKkiRJkmbJFilJkiRJmiVbpCRJkiRplqxISZIkSdIsWZGSJEmSpFmyIiVJkiRJs2RFSpIkSZJm6f8DlUY3fBJ7lv8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x216 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": "markdown",
   "metadata": {},
   "source": [
    "### Elastic-Net regularization\n",
    "- combines Ridge & Lasso methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ElasticNetCV(alphas=None, copy_X=True, cv='warn', eps=0.001, fit_intercept=True,\n",
       "             l1_ratio=0.5, max_iter=1000, n_alphas=100, n_jobs=None,\n",
       "             normalize=False, positive=False, precompute='auto',\n",
       "             random_state=None, selection='cyclic', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.ElasticNetCV()\n",
    "model.fit(X_all, y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.21273263409911083, 0.5)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alpha & l1 ratio params found by cross-validation\n",
    "model.alpha_, model.l1_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4579.324991451532, 10751.65722465327)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_train = y_train - model.predict(X_train)\n",
    "sse_train = sum(resid_train**2)\n",
    "\n",
    "resid_test = y_test - model.predict(X_test)\n",
    "sse_test = sum(resid_test**2)\n",
    "\n",
    "sse_test, sse_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 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
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((150, 4), (150,))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape, iris.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(\n",
    "    iris.data, \n",
    "    iris.target, \n",
    "    train_size=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = linear_model.LogisticRegression()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# classifier trained; # use it for predicting class for new observations\n",
    "y_test_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      0.93      0.96        14\n",
      "           2       0.92      1.00      0.96        12\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.97      0.98      0.97        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": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16, 17, 12])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0,  0],\n",
       "       [ 0, 13,  1],\n",
       "       [ 0,  0, 12]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusion matrix:\n",
    "# diagonals = # of samples correctly classified for each category level\n",
    "# off diagonals = # of incorrectly classified samples\n",
    "metrics.confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic decision tree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0,  0],\n",
       "       [ 0, 13,  1],\n",
       "       [ 0,  1, 11]])"
      ]
     },
     "execution_count": 72,
     "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": "markdown",
   "metadata": {},
   "source": [
    "### K-Neighbors classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0,  0],\n",
       "       [ 0, 13,  1],\n",
       "       [ 0,  1, 11]])"
      ]
     },
     "execution_count": 73,
     "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": "markdown",
   "metadata": {},
   "source": [
    "### SVC (Support Vector classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[19,  0,  0],\n",
       "       [ 0, 13,  1],\n",
       "       [ 0,  1, 11]])"
      ]
     },
     "execution_count": 74,
     "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": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0],\n",
       "       [ 0, 16,  1],\n",
       "       [ 0,  0, 12]])"
      ]
     },
     "execution_count": 68,
     "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": "markdown",
   "metadata": {},
   "source": [
    "### Apply multi classifiers to same problem - compare performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_size_vec = np.linspace(0.1, 0.9, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifiers = [tree.DecisionTreeClassifier,\n",
    "               neighbors.KNeighborsClassifier,\n",
    "               svm.SVC,\n",
    "               ensemble.RandomForestClassifier\n",
    "              ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cm_diags = np.zeros((3, len(train_size_vec), len(classifiers)), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "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": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 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\n",
    "\n",
    "- Type of classification problem where the classes are unknown\n",
    "- Returns array of integers that assigns each sample to a cluster (class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X, y = iris.data, iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(123)\n",
    "n_clusters = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cluster.KMeans(n_clusters=n_clusters)\n",
    "c.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": 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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = c.predict(X)\n",
    "y_pred[::8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": 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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[::8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# to compare two arrays, need to rename elements so\n",
    "# the same integer values are used for the same group\n",
    "\n",
    "idx_0, idx_1, idx_2 = (np.where(y_pred == n) for n in range(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred[idx_0], y_pred[idx_1], y_pred[idx_2] = 2, 0, 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": 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],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[::8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  0,  0],\n",
       "       [ 0, 48,  2],\n",
       "       [ 0, 14, 36]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we can summarize overlaps btwn supervised & unsupervised Iris classification\n",
    "# using a confusion matrix.\n",
    "metrics.confusion_matrix(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 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\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Above:\n",
    "- Scatter plots for each pair of features\n",
    "- Red squares (hard to see) drawn around each sample where clustering doesn't agree with supervised classification."
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}