notebook/ch11_machine_learning.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import math, random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def split_data(data, prob):\n",
    "    \"\"\"split data into fractions [prob, 1 - prob]\"\"\"\n",
    "    results = [], []\n",
    "    for row in data:\n",
    "        results[0 if random.random() < prob else 1].append(row)\n",
    "    return results\n",
    "\n",
    "def train_test_split(x, y, test_pct):\n",
    "    data = list(zip(x, y))                        # pair corresponding values\n",
    "    train, test = split_data(data, 1 - test_pct)  # split the dataset of pairs\n",
    "    x_train, y_train = list(zip(*train))          # magical un-zip trick\n",
    "    x_test, y_test = list(zip(*test))\n",
    "    return x_train, x_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris_data ={\n",
    "\"Sepal.Length\": [5.1,4.9,4.7,4.6,5,5.4,4.6,5,4.4,4.9,5.4,4.8,\n",
    "                 4.8,4.3,5.8,5.7,5.4,5.1,5.7,5.1,5.4,5.1,4.6,\n",
    "                 5.1,4.8,5,5,5.2,5.2,4.7,4.8,5.4,5.2,5.5,4.9,\n",
    "                 5,5.5,4.9,4.4,5.1,5,4.5,4.4,5,5.1,4.8,5.1,4.6,5.3,5], \n",
    "\"Sepal.Width\": [3.5,3,3.2,3.1,3.6,3.9,3.4,3.4,2.9,3.1,3.7,3.4,3,3,4,\n",
    "                4.4,3.9,3.5,3.8,3.8,3.4,3.7,3.6,3.3,3.4,3,3.4,3.5,\n",
    "                3.4,3.2,3.1,3.4,4.1,4.2,3.1,3.2,3.5,3.6,3,3.4,\n",
    "                3.5,2.3,3.2,3.5,3.8,3,3.8,3.2,3.7,3.3]\n",
    "}\n",
    "\n",
    "training_data,test_data =  split_data(iris_data[\"Sepal.Length\"], 0.66)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(iris_data[\"Sepal.Length\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data) # 학습후 평가대상이 되는 데이터 1/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(training_data) # 학습을 위한 데이터 2/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_yest = train_test_split(iris_data[\"Sepal.Length\"], iris_data[\"Sepal.Width\"], 0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy(70, 4930, 13930, 981070) 0.98114\n",
      "precision(70, 4930, 13930, 981070) 0.014\n",
      "recall(70, 4930, 13930, 981070) 0.005\n",
      "f1_score(70, 4930, 13930, 981070) 0.00736842105263158\n"
     ]
    }
   ],
   "source": [
    "def accuracy(tp, fp, fn, tn):\n",
    "    correct = tp + tn\n",
    "    total = tp + fp + fn + tn\n",
    "    return correct / total\n",
    "\n",
    "def precision(tp, fp, fn, tn):\n",
    "    return tp / (tp + fp)\n",
    "\n",
    "def recall(tp, fp, fn, tn):\n",
    "    return tp / (tp + fn)\n",
    "\n",
    "def f1_score(tp, fp, fn, tn):\n",
    "    p = precision(tp, fp, fn, tn)\n",
    "    r = recall(tp, fp, fn, tn)\n",
    "\n",
    "    return 2 * p * r / (p + r)\n",
    "\n",
    "print(\"accuracy(70, 4930, 13930, 981070)\", accuracy(70, 4930, 13930, 981070))\n",
    "print(\"precision(70, 4930, 13930, 981070)\", precision(70, 4930, 13930, 981070))\n",
    "print(\"recall(70, 4930, 13930, 981070)\", recall(70, 4930, 13930, 981070))\n",
    "print(\"f1_score(70, 4930, 13930, 981070)\", f1_score(70, 4930, 13930, 981070))"
   ]
  }
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