scikit-learnの決定木をルールベースのコードに変換

DecisionTree2Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-learnの決定木をルールベースのコードに変換"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 変換コードのダウンロード"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['--2016-01-09 04:18:48--  https://raw.githubusercontent.com/ikegami-yukino/misc/master/machinelearning/dt2code.py',\n",
       " 'Resolving raw.githubusercontent.com... 103.245.222.133',\n",
       " 'Connecting to raw.githubusercontent.com|103.245.222.133|:443... connected.',\n",
       " 'HTTP request sent, awaiting response... 200 OK',\n",
       " 'Length: 2001 (2.0K) [text/plain]',\n",
       " \"Saving to: 'dt2code.py.1'\",\n",
       " '',\n",
       " '     0K .                                                     100% 90.9M=0s',\n",
       " '',\n",
       " \"2016-01-09 04:18:48 (90.9 MB/s) - 'dt2code.py.1' saved [2001/2001]\",\n",
       " '']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%system wget https://raw.githubusercontent.com/ikegami-yukino/misc/master/machinelearning/dt2code.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## irisのデータで試してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def f(sepal_length=0, sepal_width=0, petal_length=0, petal_width=0):\n",
      "    \"\"\"\n",
      "    0 -> setosa\n",
      "    1 -> versicolor\n",
      "    2 -> virginica\n",
      "    \"\"\"\n",
      "    if petal_length <= 2.45000004768:  # samples=150\n",
      "        return 0  # samples=50\n",
      "    else:\n",
      "        if petal_width <= 1.75:  # samples=100\n",
      "            if petal_length <= 4.94999980927:  # samples=54\n",
      "                if petal_width <= 1.65000009537:  # samples=48\n",
      "                    return 1  # samples=47\n",
      "                else:\n",
      "                    return 2  # samples=1\n",
      "            else:\n",
      "                if petal_width <= 1.54999995232:  # samples=6\n",
      "                    return 2  # samples=3\n",
      "                else:\n",
      "                    if petal_length <= 5.44999980927:  # samples=3\n",
      "                        return 1  # samples=2\n",
      "                    else:\n",
      "                        return 2  # samples=1\n",
      "        else:\n",
      "            if petal_length <= 4.85000038147:  # samples=46\n",
      "                if sepal_width <= 3.09999990463:  # samples=3\n",
      "                    return 2  # samples=2\n",
      "                else:\n",
      "                    return 1  # samples=1\n",
      "            else:\n",
      "                return 2  # samples=43\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn import tree\n",
    "import dt2code\n",
    "iris = load_iris()\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(iris.data, iris.target)\n",
    "print(dt2code.dt2code(clf, iris.feature_names, iris.target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 得られたコードはこんな風にして使える"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def predict(X):\n",
    "    def f(sepal_length=0, sepal_width=0, petal_length=0, petal_width=0):\n",
    "        \"\"\"\n",
    "        0 -> setosa\n",
    "        1 -> versicolor\n",
    "        2 -> virginica\n",
    "        \"\"\"\n",
    "        if petal_length <= 2.45000004768:  # samples=150\n",
    "            return 0  # samples=50\n",
    "        else:\n",
    "            if petal_width <= 1.75:  # samples=100\n",
    "                if petal_length <= 4.94999980927:  # samples=54\n",
    "                    if petal_width <= 1.65000009537:  # samples=48\n",
    "                        return 1  # samples=47\n",
    "                    else:\n",
    "                        return 2  # samples=1\n",
    "                else:\n",
    "                    if petal_width <= 1.54999995232:  # samples=6\n",
    "                        return 2  # samples=3\n",
    "                    else:\n",
    "                        if petal_length <= 5.44999980927:  # samples=3\n",
    "                            return 1  # samples=2\n",
    "                        else:\n",
    "                            return 2  # samples=1\n",
    "            else:\n",
    "                if petal_length <= 4.85000038147:  # samples=46\n",
    "                    if sepal_width <= 3.09999990463:  # samples=3\n",
    "                        return 2  # samples=2\n",
    "                    else:\n",
    "                        return 1  # samples=1\n",
    "                else:\n",
    "                    return 2  # samples=43\n",
    "\n",
    "    return list(map(lambda x: f(*x), X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict([[5.1,  3.5,  1.4,  0.2], [5, 2, 3.5, 1], [6.3, 3.3, 6, 2.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[5.1,  3.5,  1.4,  0.2], [5, 2, 3.5, 1], [6.3, 3.3, 6, 2.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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