week-3-iris-classification-example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "X, y = load_iris(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = (y==2)*1\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True, False, False,\n",
       "        True,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True, False])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred==y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8947368421052632"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = (y_pred==y_test).mean()\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96666667, 0.96666667, 0.86666667, 0.73333333, 0.93333333])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(gnb, X, y, cv=5)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "y_pred = cross_val_predict(gnb, X, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[86, 14],\n",
       "       [ 2, 48]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "conf_mx = confusion_matrix(y, y_pred)\n",
    "conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7741935483870968"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision = 48 /(14+48)\n",
    "precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall = 48 /(2+48)\n",
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(gnb, X, y, cv=5, method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00000000e+00, 6.20658885e-24],\n",
       "       [1.00000000e+00, 6.68085016e-24],\n",
       "       [1.00000000e+00, 7.57267703e-25],\n",
       "       [1.00000000e+00, 9.55464806e-24],\n",
       "       [1.00000000e+00, 3.42138851e-24],\n",
       "       [1.00000000e+00, 1.48662698e-20],\n",
       "       [1.00000000e+00, 1.43230826e-23],\n",
       "       [1.00000000e+00, 2.26676275e-23],\n",
       "       [1.00000000e+00, 1.29658497e-24],\n",
       "       [1.00000000e+00, 2.53076679e-24],\n",
       "       [1.00000000e+00, 3.26536423e-23],\n",
       "       [1.00000000e+00, 4.58773249e-23],\n",
       "       [1.00000000e+00, 4.88324264e-25],\n",
       "       [1.00000000e+00, 1.27309246e-27],\n",
       "       [1.00000000e+00, 4.27432902e-25],\n",
       "       [1.00000000e+00, 1.42618489e-22],\n",
       "       [1.00000000e+00, 6.60179839e-23],\n",
       "       [1.00000000e+00, 5.56876348e-23],\n",
       "       [1.00000000e+00, 6.75170061e-21],\n",
       "       [1.00000000e+00, 8.06726254e-23],\n",
       "       [1.00000000e+00, 3.68803149e-22],\n",
       "       [1.00000000e+00, 1.54858236e-22],\n",
       "       [1.00000000e+00, 5.13809365e-28],\n",
       "       [1.00000000e+00, 9.27709725e-20],\n",
       "       [1.00000000e+00, 1.02622291e-21],\n",
       "       [1.00000000e+00, 6.22219750e-23],\n",
       "       [1.00000000e+00, 1.91126920e-21],\n",
       "       [1.00000000e+00, 8.83028174e-24],\n",
       "       [1.00000000e+00, 3.00042577e-24],\n",
       "       [1.00000000e+00, 2.19132480e-23],\n",
       "       [1.00000000e+00, 3.34425897e-23],\n",
       "       [1.00000000e+00, 1.37776232e-21],\n",
       "       [1.00000000e+00, 1.88988969e-26],\n",
       "       [1.00000000e+00, 3.55899934e-26],\n",
       "       [1.00000000e+00, 1.06989145e-23],\n",
       "       [1.00000000e+00, 1.42194627e-25],\n",
       "       [1.00000000e+00, 1.04506307e-24],\n",
       "       [1.00000000e+00, 6.19769808e-26],\n",
       "       [1.00000000e+00, 1.40831337e-25],\n",
       "       [1.00000000e+00, 9.76780305e-24],\n",
       "       [1.00000000e+00, 1.93212724e-23],\n",
       "       [1.00000000e+00, 7.13431569e-24],\n",
       "       [1.00000000e+00, 1.05819400e-24],\n",
       "       [1.00000000e+00, 3.48114329e-19],\n",
       "       [1.00000000e+00, 1.86712394e-19],\n",
       "       [1.00000000e+00, 1.09840616e-22],\n",
       "       [1.00000000e+00, 6.84452617e-23],\n",
       "       [1.00000000e+00, 6.84271196e-24],\n",
       "       [1.00000000e+00, 4.44184332e-23],\n",
       "       [1.00000000e+00, 1.54892150e-23],\n",
       "       [1.49650980e-01, 8.50349020e-01],\n",
       "       [5.23650567e-01, 4.76349433e-01],\n",
       "       [3.85160877e-02, 9.61483912e-01],\n",
       "       [9.99594615e-01, 4.05385306e-04],\n",
       "       [2.88369381e-01, 7.11630619e-01],\n",
       "       [9.81267964e-01, 1.87320358e-02],\n",
       "       [2.41582069e-01, 7.58417931e-01],\n",
       "       [9.99999983e-01, 1.74736171e-08],\n",
       "       [6.89454132e-01, 3.10545868e-01],\n",
       "       [9.99484862e-01, 5.15138421e-04],\n",
       "       [9.99999974e-01, 2.60432873e-08],\n",
       "       [8.51575237e-01, 1.48424763e-01],\n",
       "       [9.99967662e-01, 3.23376111e-05],\n",
       "       [4.87557105e-01, 5.12442895e-01],\n",
       "       [9.99822873e-01, 1.77127033e-04],\n",
       "       [3.54216922e-01, 6.45783078e-01],\n",
       "       [7.91092881e-01, 2.08907119e-01],\n",
       "       [9.99929664e-01, 7.03355315e-05],\n",
       "       [5.24739524e-01, 4.75260476e-01],\n",
       "       [9.99948754e-01, 5.12462787e-05],\n",
       "       [3.48496884e-02, 9.65150312e-01],\n",
       "       [9.88890125e-01, 1.11098747e-02],\n",
       "       [7.89698832e-02, 9.21030117e-01],\n",
       "       [9.32602624e-01, 6.73973758e-02],\n",
       "       [8.75639079e-01, 1.24360921e-01],\n",
       "       [3.99289111e-01, 6.00710889e-01],\n",
       "       [5.40239608e-02, 9.45976039e-01],\n",
       "       [1.96883865e-03, 9.98031161e-01],\n",
       "       [4.76976202e-01, 5.23023798e-01],\n",
       "       [9.99998795e-01, 1.20508974e-06],\n",
       "       [9.99940968e-01, 5.90323288e-05],\n",
       "       [9.99992285e-01, 7.71455393e-06],\n",
       "       [9.98861763e-01, 1.13823743e-03],\n",
       "       [4.39543439e-02, 9.56045656e-01],\n",
       "       [8.43885844e-01, 1.56114156e-01],\n",
       "       [5.08930706e-01, 4.91069294e-01],\n",
       "       [7.81853322e-02, 9.21814668e-01],\n",
       "       [8.89641824e-01, 1.10358176e-01],\n",
       "       [9.94289970e-01, 5.71003012e-03],\n",
       "       [9.97346953e-01, 2.65304698e-03],\n",
       "       [9.93675485e-01, 6.32451488e-03],\n",
       "       [5.98251524e-01, 4.01748476e-01],\n",
       "       [9.98080749e-01, 1.91925114e-03],\n",
       "       [9.99999884e-01, 1.15605583e-07],\n",
       "       [9.88899605e-01, 1.11003949e-02],\n",
       "       [9.96284696e-01, 3.71530429e-03],\n",
       "       [9.86367012e-01, 1.36329878e-02],\n",
       "       [9.20611471e-01, 7.93885290e-02],\n",
       "       [9.99999913e-01, 8.69066414e-08],\n",
       "       [9.91348095e-01, 8.65190489e-03],\n",
       "       [3.12385881e-03, 9.96876141e-01],\n",
       "       [7.49240276e-02, 9.25075972e-01],\n",
       "       [4.56937395e-04, 9.99543063e-01],\n",
       "       [1.35775768e-02, 9.86422423e-01],\n",
       "       [1.53583452e-03, 9.98464165e-01],\n",
       "       [3.46972589e-04, 9.99653027e-01],\n",
       "       [9.76170448e-01, 2.38295517e-02],\n",
       "       [1.46492427e-03, 9.98535076e-01],\n",
       "       [7.38920018e-03, 9.92610800e-01],\n",
       "       [7.57821479e-04, 9.99242179e-01],\n",
       "       [8.14505012e-03, 9.91854950e-01],\n",
       "       [1.12437172e-02, 9.88756283e-01],\n",
       "       [1.06690740e-03, 9.98933093e-01],\n",
       "       [1.41491061e-01, 8.58508939e-01],\n",
       "       [1.99758889e-02, 9.80024111e-01],\n",
       "       [3.18002417e-03, 9.96819976e-01],\n",
       "       [7.95989574e-03, 9.92040104e-01],\n",
       "       [3.53948583e-03, 9.96460514e-01],\n",
       "       [6.58804953e-04, 9.99341195e-01],\n",
       "       [8.91469417e-01, 1.08530583e-01],\n",
       "       [1.05966454e-04, 9.99894034e-01],\n",
       "       [5.85481271e-02, 9.41451873e-01],\n",
       "       [2.28500689e-05, 9.99977150e-01],\n",
       "       [2.41162231e-02, 9.75883777e-01],\n",
       "       [4.11274571e-04, 9.99588725e-01],\n",
       "       [2.51176514e-04, 9.99748823e-01],\n",
       "       [4.34625811e-02, 9.56537419e-01],\n",
       "       [4.31238127e-02, 9.56876187e-01],\n",
       "       [7.88330258e-04, 9.99211670e-01],\n",
       "       [9.39418850e-04, 9.99060581e-01],\n",
       "       [5.36121362e-06, 9.99994639e-01],\n",
       "       [9.63533914e-06, 9.99990365e-01],\n",
       "       [6.26599293e-05, 9.99937340e-01],\n",
       "       [3.69968852e-02, 9.63003115e-01],\n",
       "       [8.62467930e-02, 9.13753207e-01],\n",
       "       [3.74859841e-07, 9.99999625e-01],\n",
       "       [1.79369897e-04, 9.99820630e-01],\n",
       "       [8.69922485e-04, 9.99130078e-01],\n",
       "       [1.76124952e-02, 9.82387505e-01],\n",
       "       [3.02093188e-05, 9.99969791e-01],\n",
       "       [1.13493847e-04, 9.99886506e-01],\n",
       "       [2.02619092e-04, 9.99797381e-01],\n",
       "       [1.00556210e-02, 9.89944379e-01],\n",
       "       [7.73747805e-05, 9.99922625e-01],\n",
       "       [1.59903456e-04, 9.99840097e-01],\n",
       "       [2.31748340e-04, 9.99768252e-01],\n",
       "       [3.98183798e-03, 9.96018162e-01],\n",
       "       [8.64907077e-04, 9.99135093e-01],\n",
       "       [1.32657052e-03, 9.98673429e-01],\n",
       "       [1.59278431e-02, 9.84072157e-01]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_pos = y_scores[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y, y_scores_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.65789474, 0.65333333, 0.66216216, 0.67123288, 0.66666667,\n",
       "       0.67605634, 0.68571429, 0.69565217, 0.70588235, 0.71641791,\n",
       "       0.72727273, 0.73846154, 0.75      , 0.76190476, 0.77419355,\n",
       "       0.78688525, 0.8       , 0.81355932, 0.82758621, 0.84210526,\n",
       "       0.85714286, 0.87272727, 0.87037037, 0.86792453, 0.88461538,\n",
       "       0.90196078, 0.9       , 0.89795918, 0.91666667, 0.93617021,\n",
       "       0.93478261, 0.93333333, 0.95454545, 0.95348837, 0.97619048,\n",
       "       0.97560976, 0.975     , 0.97435897, 0.97368421, 0.97297297,\n",
       "       0.97222222, 0.97142857, 0.97058824, 0.96969697, 0.96875   ,\n",
       "       0.96774194, 0.96666667, 0.96551724, 0.96428571, 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        ])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.legend(loc=\"center right\", fontsize=16) # Not shown in the book\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)        # Not shown\n",
    "    plt.grid(True)                              # Not shown\n",
    "    plt.axis([0, 1, 0, 1])             # Not shown\n",
    "\n",
    "\n",
    "\n",
    "recall_90_precision = recalls[np.argmax(precisions >= 0.90)]\n",
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 4))                                                                  # Not shown\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.86      0.91       100\n",
      "           1       0.77      0.96      0.86        50\n",
      "\n",
      "    accuracy                           0.89       150\n",
      "   macro avg       0.88      0.91      0.89       150\n",
      "weighted avg       0.91      0.89      0.90       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y, y_pred))"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}