Random_Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 隨機森林分類器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DESCR': 'Iris Plants Database\\n====================\\n\\nNotes\\n-----\\nData Set Characteristics:\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML iris datasets.\\nhttp://archive.ics.uci.edu/ml/datasets/Iris\\n\\nThe famous Iris database, first used by Sir R.A Fisher\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\nReferences\\n----------\\n   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...\\n',\n",
       " 'data': 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, 0.1],\n",
       "        [5. , 3.2, 1.2, 0.2],\n",
       "        [5.5, 3.5, 1.3, 0.2],\n",
       "        [4.9, 3.1, 1.5, 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]]),\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'target': 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]),\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10')}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=iris.data\n",
    "y=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc=RandomForestClassifier(n_estimators=5)\n",
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 0, 1, 1, 2, 0, 1, 2, 1, 0, 0, 2, 0, 0, 2, 2, 0, 0, 1, 0,\n",
       "       2, 2, 2, 2, 1, 0, 1, 2, 0, 1, 0, 1, 2, 0, 1, 2, 1, 2, 0, 2, 0, 1,\n",
       "       2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict=rfc.predict(X_test)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9555555555555556"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rfc=RandomForestClassifier(n_estimators=100,n_jobs = -1,random_state =50, min_samples_leaf = 10)\n",
    "# rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp=rfc.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08721708, 0.08863298, 0.28750636, 0.53664358])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names=iris.feature_names\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zip(imp,names)\n",
    "imp, names= zip(*sorted(zip(imp,names)))\n",
    "plt.barh(range(len(names)),imp,align='center')\n",
    "plt.yticks(range(len(names)),names)\n",
    "plt.xlabel('Importance of Features')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Importance of Each Feature')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}