10-Classification-Performance-Measures/06-Precision-Recall-Curve/06-Precision-Recall-Curve.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 精准度-召回率曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X = digits.data\n",
    "y = digits.target.copy()\n",
    "\n",
    "y[digits.target==9] = 1\n",
    "y[digits.target!=9] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "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, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)\n",
    "decision_scores = log_reg.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "precisions = []\n",
    "recalls = []\n",
    "thresholds = np.arange(np.min(decision_scores), np.max(decision_scores), 0.1)\n",
    "for threshold in thresholds:\n",
    "    y_predict = np.array(decision_scores >= threshold, dtype='int')\n",
    "    precisions.append(precision_score(y_test, y_predict))\n",
    "    recalls.append(recall_score(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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bzeE1CnLJQHl65vJHrvvwtgt/AsdcNLL1DJDCXUZcWzzBpj3trNnWwpbGTlo6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dda2b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(thresholds, precisions)\n",
    "plt.plot(thresholds, recalls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision-Recall 曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IZyOiOyLuPMPx342IJyNiR0Q8EhHr6l+qJKlWVcM9ItqAu4D1wFrg1ohYO6rZz4H/kJlv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dde0cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(precisions, recalls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scikit-learn中的Precision-Recall曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_test, decision_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precisions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recalls.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VteS0SXI7mkj4aNkJLp/hFPyWz52VnCRshXW5l1fW8MCcfP61ehfllbV0SEvg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\nXUQkAqncRUQikMpdRCQC/T/h2z9YI1kP+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111763ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(thresholds, precisions[:-1])\n",
    "plt.plot(thresholds, recalls[:-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111763e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(precisions, recalls)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}