decision tree and random forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree\n",
    "\n",
    "&nbsp;\n",
    "\n",
    "Decision tree is one of the most intuitive machine learning algorithms.\n",
    "\n",
    "We use decision tree in our life a lot except we dont call it decision tree.\n",
    "\n",
    "Decision tree is basically a replicate of our decision-making process with a lil twitch of math.\n",
    "\n",
    "It borrows a lot of concepts from information theory to create tree branches.\n",
    "\n",
    "It is less black-box compared to other machine learning algorithms.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For information gain, please refer to the following link\n",
    "\n",
    "https://towardsdatascience.com/gini-index-vs-inFormation-entropy-7a7e4fed3fcb\n",
    "\n",
    "For tree splitting, please refer to the following link\n",
    "\n",
    "https://lethalbrains.com/learn-ml-algorithms-by-coding-decision-trees-439ac503c9a4\n",
    "\n",
    "For tree building, please refer to the following link\n",
    "\n",
    "https://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#decision tree has a very similar structure to binary tree\n",
    "#if you dont know binary tree, i strongly recommend you to read the following link\n",
    "#  https://runestone.academy/runestone/books/published/pythonds/Trees/toctree.html\n",
    "#even though this repository is about reinventing the wheel\n",
    "#you never truly understands how a wheel works until you reinvent it\n",
    "#i quoted myself at the beginning of this repository\n",
    "#i am not gonna rebuild a tree structure here\n",
    "#instead I will use network package to create the data structure\n",
    "#and it is good for our final visualization of the tree structure\n",
    "#i have reinvented graph structure in my repository graph theory\n",
    "# https://github.com/je-suis-tm/graph-theory/blob/master/graph.py\n",
    "#even for reinventing the wheel, I only reinvent once, so networkx it is\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import copy\n",
    "import random as rd\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#there two types of impurity here, entropy and information theory\n",
    "#of course you can use miscalculation error\n",
    "#i just don’t find it very convincing, thats all\n",
    "def impurity(data,method):\n",
    "\n",
    "    output=0.0\n",
    "    \n",
    "    for i in set(data):      \n",
    "                    \n",
    "        prob=list(data).count(i)/len(data)\n",
    "        \n",
    "        if method=='gini':\n",
    "            output+=prob**2\n",
    "        \n",
    "        #entropy\n",
    "        else:\n",
    "            output+=prob*(np.log(prob)/np.log(2))\n",
    "    \n",
    "    extra=1 if method=='gini' else 0\n",
    "    \n",
    "    return extra-output\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#information gain is function built upon impurity\n",
    "def information_gain(parent,left,right,method):\n",
    "    \n",
    "    leftc=len(left)/len(parent)\n",
    "    rightc=len(right)/len(parent)\n",
    "    \n",
    "    sumc=leftc*impurity(left,method)+rightc*impurity(right,method)\n",
    "    \n",
    "    return impurity(parent,method)-sumc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#when we need to create tree branches (split a node into two children)\n",
    "#we would love to select a criterion that maximizes information gain\n",
    "#in human language, we wanna have two chaotic children nodes after the split\n",
    "#when the child node shows non dominant class within the group\n",
    "#it has the largest entropy (uncertainty) which maximizes information gain\n",
    "def node_split(df,x,y,method):\n",
    "    \n",
    "    info=0.0\n",
    "    target=0.0\n",
    "    \n",
    "    for i in df[x].tolist():\n",
    "        \n",
    "        #values smaller than the threshold go to left child node\n",
    "        left=df[y].loc[df[x]<i].tolist()\n",
    "        right=df[y].loc[df[x]>=i].tolist()\n",
    "        \n",
    "        temp=information_gain(df[y].tolist(),left,right,method)\n",
    "            \n",
    "        if info<temp:\n",
    "            info=temp\n",
    "            target=i\n",
    "    \n",
    "    #we output both split threshold and its information gain\n",
    "    return target,info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this function examines all the potential split threshold\n",
    "#we can split from the current input factor or use another input factor\n",
    "#at the end, we only select the criterion that has the largest information gain\n",
    "def factors_split(data,y,method,random=False):\n",
    "    \n",
    "    output=pd.DataFrame()\n",
    "    \n",
    "    #random argument is used for random forest\n",
    "    #unlike conventional decision tree\n",
    "    #we dont compare information gain from all the input features\n",
    "    #we randomly select a couple of features for every split\n",
    "    #and compare the information gain of these randomly selected features\n",
    "    if random==True:\n",
    "        cols=[i for i in data.columns if i!=y]\n",
    "        bagging=rd.sample(cols,int(len(cols)**0.5))\n",
    "        bagging.append(y)\n",
    "        df=copy.deepcopy(data)[bagging]\n",
    "    else:\n",
    "        df=copy.deepcopy(data)\n",
    "    \n",
    "    for i in df.columns:\n",
    "        \n",
    "        if i!=y:\n",
    "\n",
    "            target,info=node_split(df,i,y,method)\n",
    "            output[i]=[target,info]\n",
    "        \n",
    "    output=output.T\n",
    "    output.reset_index(inplace=True)\n",
    "    output.columns=['colname','target','info']\n",
    "    \n",
    "    #sort by information gain in descending order\n",
    "    output.sort_values(by='info',inplace=True,\n",
    "                       ascending=False)\n",
    "    output.reset_index(inplace=True,drop=True)\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#like graph theory, data structure plays a bigger role than algorithm\n",
    "#how to build our tree properly is crucial to our model\n",
    "#tree building is done recursively\n",
    "#if you are not confident with your knowledge in recursion\n",
    "# https://github.com/je-suis-tm/recursion-and-dynamic-programming\n",
    "def build_tree(df,parentname,tree,xcol,ycol,\n",
    "               max_tree_depth=10,min_node_num=2,\n",
    "               method='gini',**kwargs):\n",
    "\n",
    "    temp=df.loc[tree.nodes[parentname]['data']]\n",
    "    \n",
    "    #this determines how to split to have the largest information gain\n",
    "    guide=factors_split(temp,ycol,method,**kwargs)\n",
    "    \n",
    "    target=guide['target'][0]\n",
    "    colname=guide['colname'][0]\n",
    "    \n",
    "    left=temp.loc[temp[colname]<target].index.tolist()\n",
    "    right=temp.loc[temp[colname]>=target].index.tolist()\n",
    "    \n",
    "    #here are the conditions to build children nodes\n",
    "    \n",
    "    #we need to preset a maximum tree depth\n",
    "    #the deeper the tree is, the more likely it will be overfitted\n",
    "    #one of the biggest flaws of decision tree is overfit\n",
    "    #we can apply pruning to clean up the tree after we get the model\n",
    "    #when we reach the maximum tree depth\n",
    "    #we call it an end to splitting\n",
    "    #we take the majority within the group as classification\n",
    "    if len(parentname)>=max_tree_depth: \n",
    "        ind=tree.nodes[parentname]['data']\n",
    "        nx.set_node_attributes(tree,\n",
    "                               {parentname:\n",
    "                                {'label':max(df[ycol].loc[ind].tolist(),\n",
    "                                             key=df[ycol].loc[ind].tolist().count)}})\n",
    "        \n",
    "        return\n",
    "\n",
    "    #there could be a case where the biggest information gain leaves all elements to one child node\n",
    "    #the other child node has nothing at all\n",
    "    #we should immediately stop splitting and pick the majority as the classification\n",
    "    if not left or not right:\n",
    "\n",
    "        ind=tree.nodes[parentname]['data']\n",
    "        nx.set_node_attributes(tree,{parentname:\n",
    "                                     {'label':max(df[ycol].loc[ind].tolist(),\n",
    "                                                  key=df[ycol].loc[ind].tolist().count)}})\n",
    "        \n",
    "        return\n",
    "    \n",
    "    #if the data inside the child node has homogenous classification\n",
    "    #there is no need for further splitting\n",
    "    #but we wont return the function here cuz this is only the case for left child node\n",
    "    if len(df[ycol].loc[left].unique())==1:\n",
    "        \n",
    "        #when we create a left child node, we just add L to its node name\n",
    "        #we can track the depth of current node easily by this naming rule\n",
    "        tree.add_node(f'{parentname}L',data=left,\n",
    "                      target=target,colname=colname,\n",
    "                      label=df[ycol].loc[left].iloc[0]\n",
    "                     )\n",
    "\n",
    "        tree.add_edge(f'{parentname}', f'{parentname}L')\n",
    "\n",
    "\n",
    "    else:\n",
    "        \n",
    "        #there is another preset parameter, minimum data inside the node\n",
    "        #if we have too few samples inside the node\n",
    "        #our split becomes meaningless\n",
    "        #if we have enough samples inside the node\n",
    "        #we continue splitting and call our function recursively\n",
    "        if len(left)>min_node_num:\n",
    "            tree.add_node(f'{parentname}L',data=left,\n",
    "                          target=target,colname=colname\n",
    "                         )\n",
    "\n",
    "            tree.add_edge(f'{parentname}', f'{parentname}L')\n",
    "            \n",
    "            build_tree(df,f'{parentname}L',tree,\n",
    "                       xcol,ycol,method=method,**kwargs)\n",
    "\n",
    "        else:\n",
    "            tree.add_node(f'{parentname}L',data=left,\n",
    "                          target=target,colname=colname,\n",
    "                          label=max(df[ycol].loc[left].tolist(),\n",
    "                                    key=df[ycol].loc[left].tolist().count)\n",
    "                         )\n",
    "\n",
    "            tree.add_edge(f'{parentname}', f'{parentname}L')\n",
    "\n",
    "\n",
    "    #the right child node has the same case as the left one\n",
    "    if len(df[ycol].loc[right].unique())==1:\n",
    "        tree.add_node(f'{parentname}R',data=right,\n",
    "                      target=target,colname=colname,\n",
    "                      label=df[ycol].loc[right].iloc[0]\n",
    "                     )\n",
    "\n",
    "        tree.add_edge(f'{parentname}', f'{parentname}R')\n",
    "\n",
    "    else:\n",
    "\n",
    "        if len(right)>min_node_num:\n",
    "            tree.add_node(f'{parentname}R',data=right,\n",
    "                          target=target,colname=colname\n",
    "                         )\n",
    "            tree.add_edge(f'{parentname}', f'{parentname}R')\n",
    "            \n",
    "            build_tree(df,f'{parentname}R',tree,\n",
    "                       xcol,ycol,method=method,**kwargs)\n",
    "\n",
    "        else:\n",
    "            tree.add_node(f'{parentname}R',data=left,\n",
    "                          target=target,colname=colname,\n",
    "                          label=max(df[ycol].loc[left].tolist(),\n",
    "                                    key=df[ycol].loc[left].tolist().count)\n",
    "                         )\n",
    "\n",
    "            tree.add_edge(f'{parentname}', f'{parentname}R')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#once we have our tree structure built, prediction is simple\n",
    "#it is kinda like traversal algorithm in graph theory\n",
    "#it travels down the tree structure like depth first search\n",
    "# https://github.com/je-suis-tm/graph-theory/blob/master/BFS%20DFS%20on%20DCG.ipynb\n",
    "#the function itself is recursive as well\n",
    "#but when we reach the end of the tree, we do not go one level up\n",
    "#we get the label attribute of the node and exit\n",
    "def predict(tree,nodename,data,ind):\n",
    "\n",
    "    colname=tree.nodes[nodename]['colname']\n",
    "    target=tree.nodes[nodename]['target']\n",
    "    edges=[i[1] for i in tree.edges(nodename)]\n",
    "    \n",
    "    if not edges:\n",
    "        forecast=tree.nodes[nodename]['label']\n",
    "        return forecast\n",
    "    \n",
    "    if data[colname].iloc[ind]<target:\n",
    "        nodename=edges[0]\n",
    "    else:\n",
    "        nodename=edges[1]\n",
    "        \n",
    "    forecast=predict(tree,nodename,data,ind)\n",
    "    \n",
    "    return forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is a self-made function to create coordinates for a perfect binary tree\n",
    "#graph visualization in networkx is problematic\n",
    "#random positions do not fit for a perfect binary tree\n",
    "#the function borrows an idea from how to plot a pascal triangle\n",
    "# https://github.com/je-suis-tm/recursion-and-dynamic-programming/blob/master/pascal%20triangle%20better%20structured.py\n",
    "#though no recursion is implemented\n",
    "\n",
    "#xwid refers to the horizontal distance between sibling nodes\n",
    "#ywid refers to the vertical distance between parent node and children nodes\n",
    "def binary_tree_pos(tree,xwid=5,ywid=-7):    \n",
    "    \n",
    "    #this is the depth of the tree\n",
    "    #basically finding the maximum length of node names\n",
    "    rownum=max([len(i) for i in tree.nodes])\n",
    "\n",
    "    lastrow=[i*xwid for i in range(2**(rownum-1))]\n",
    "\n",
    "    xmat=[lastrow]\n",
    "    \n",
    "    #we start from the deepest level to calculate coordinates for each node\n",
    "    for i in range(rownum-1,0,-1):\n",
    "        row=[]\n",
    "        for j in range(1,len(xmat[-1]),2):\n",
    "            \n",
    "            #since we assume this is a perfect binary tree\n",
    "            #the parent node should be located right between two children nodes\n",
    "            row.append(np.mean([xmat[-1][j],xmat[-1][j-1]]))\n",
    "        xmat.append(row)\n",
    "\n",
    "    ymat=[]\n",
    "    \n",
    "    #get the correct order\n",
    "    xmat=xmat[::-1]\n",
    "    \n",
    "    #y coordinate simply depends on the depth of a node\n",
    "    ymat=[[i*ywid for j in xmat[i]] for i in range(len(xmat))]\n",
    "    \n",
    "    root='0'\n",
    "    nodes=[root]\n",
    "    \n",
    "    #using our naming rule to get a whole set of node names\n",
    "    for i in range(1,rownum):\n",
    "        row=[]\n",
    "        for j in nodes[-1]:\n",
    "            row.append(j+'L')\n",
    "            row.append(j+'R')\n",
    "\n",
    "        nodes.append(row)\n",
    "    \n",
    "    #map coordinates into the specific node\n",
    "    #even though decision tree is no perfect binary tree\n",
    "    #some of left/right do not exist\n",
    "    #as we dont create edges from parent node to non-existent child\n",
    "    #these non-existent nodes wont appear in visualization\n",
    "    pos={}\n",
    "    for row in range(len(nodes)):\n",
    "        for column in range(len(nodes[row])):\n",
    "            pos[nodes[row][column]]=(xmat[row][column],\n",
    "                                     ymat[row][column])\n",
    "            \n",
    "    return pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#concatenate all the functions\n",
    "def decision_tree(train,test,xcol,ycol,method='gini'):   \n",
    "    \n",
    "    #decision tree is a directed graph structure\n",
    "    #we can only go down but not up\n",
    "    tree=nx.DiGraph()\n",
    "\n",
    "    guide=factors_split(train,ycol,method)\n",
    "    target=guide['target'][0]\n",
    "    colname=guide['colname'][0]\n",
    "    \n",
    "    #the root node has to be added outside of build_tree function\n",
    "    tree.add_node('0',data=train.index.tolist(),\n",
    "                 target=target,colname=colname)\n",
    "\n",
    "    parentname='0'\n",
    "\n",
    "    build_tree(train,parentname,tree,\n",
    "               xcol,ycol,method=method)\n",
    "    \n",
    "    #visualization using networkx\n",
    "    ax=plt.figure(dpi=500)\n",
    "    nx.draw(tree,with_labels=True,\n",
    "            node_size=100,pos=binary_tree_pos(tree),\n",
    "            font_size=5)\n",
    "    \n",
    "    forecast=[]    \n",
    "    for i in train.index.tolist():\n",
    "\n",
    "        forecast.append(predict(tree,'0',train,i))\n",
    "\n",
    "    train['projection']=forecast\n",
    "    \n",
    "    forecast=[]\n",
    "    for i in test.index.tolist():\n",
    "\n",
    "        forecast.append(predict(tree,'0',test,i))\n",
    "\n",
    "    test['projection']=forecast\n",
    "    \n",
    "    print('\\ntrain accuracy: %s'%(len(train[train['y']==train['projection']])/len(train)))\n",
    "    print('\\ntest accuracy: %s'%(len(test[test['y']==test['projection']])/len(test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#official sklearn decision tree\n",
    "def skl_decision_tree(train,test,xcol,ycol):\n",
    "\n",
    "    clf=DecisionTreeClassifier().fit(train[xcol],train[ycol])\n",
    "    print('\\ntrain accuracy: %s'%(clf.score(train[xcol],train[ycol])))\n",
    "    print('\\ntest accuracy: %s'%(clf.score(test[xcol],test[ycol])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "xcol=['x0','x1','x2','x3']\n",
    "ycol='y'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using the classic iris data\n",
    "iris=load_iris()\n",
    "\n",
    "df=pd.DataFrame(iris.data,columns=xcol)\n",
    "\n",
    "#in decision tree, each input factor has to be float/int\n",
    "#sklearn has already done that for us\n",
    "#but if you are using the dataset inside the repository\n",
    "#you can use np.unique to convert categorical data to int\n",
    "df[ycol]=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(df[xcol],df[ycol])\n",
    "    \n",
    "train=pd.concat([x_train,y_train],axis=1)\n",
    "train.reset_index(inplace=True,drop=True)\n",
    "    \n",
    "test=pd.concat([x_test,y_test],axis=1)\n",
    "test.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "train accuracy: 0.96\n",
      "\n",
      "test accuracy: 0.96\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#homemade decision tree\n",
    "#in-sample data\n",
    "decision_tree(df,df,xcol,ycol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "train accuracy: 0.9464285714285714\n",
      "\n",
      "test accuracy: 1.0\n"
     ]
    },
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AAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAABhEHAAAAAAAAAAAAAAAAhgDekrDILaasBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#homemade decision tree\n",
    "#out-of-sample data (cross validation)\n",
    "decision_tree(train,test,xcol,ycol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "train accuracy: 1.0\n",
      "\n",
      "test accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "#sklearn official package\n",
    "#in-sample data\n",
    "skl_decision_tree(df,df,xcol,ycol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "train accuracy: 1.0\n",
      "\n",
      "test accuracy: 0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "#sklearn official package\n",
    "#out-of-sample data (cross validation)\n",
    "skl_decision_tree(train,test,xcol,ycol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for some bizarre reason\n",
    "#homemade decision tree beats sklearn in cross validation\n",
    "#however, sklearn's performance is more stable and consistent\n",
    "#homemade can be as low as 60% at accuracy in cross validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest\n",
    "\n",
    "&nbsp;\n",
    "\n",
    "Random forest is an upgraded version of decision tree.\n",
    "\n",
    "Random forest consists of multiple decision trees.\n",
    "\n",
    "There are two key points that differentiates a tree in random forest from a conventional decision tree.\n",
    "\n",
    "The first one is a technique called bagging.\n",
    "\n",
    "Bagging literally implies a subset of randomly selected non-unique data.\n",
    "\n",
    "The second one is the random selection of features for node splitting.\n",
    "\n",
    "Theoretically, this eliminates the focus on some dominant input features.\n",
    "\n",
    "With these enhancements, random forest is supposed to achieve more balanced bias and variance tradeoff than decision tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For general understanding of random forest, please refer to Stanford University materials\n",
    "\n",
    "https://web.stanford.edu/class/cs231m/lectures/lecture-8-machine-learning.pdf\n",
    "\n",
    "For deep analysis on random forest, please refer to the following link\n",
    "\n",
    "After all, this is an article on Nature, one of the most influential academic journal\n",
    "\n",
    "https://www.nature.com/articles/nmeth.4438"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the foundation of random forest is decision tree\n",
    "#the function is very similar to decision_tree\n",
    "#except there are more iterations and more randomness\n",
    "#in theory, the more decision trees we have\n",
    "#the more accurate our forecast will be\n",
    "#and the slower the performance is\n",
    "def random_forest(train,test,xcol,ycol,\n",
    "                  method='gini',treenum=10):   \n",
    "    \n",
    "    \n",
    "    oob=[]\n",
    "    training_forecast=[]\n",
    "    testing_forecast=[]\n",
    "    oob_forecast=[]\n",
    "    testing=copy.deepcopy(test)\n",
    "    \n",
    "    for i in range(treenum):\n",
    "        \n",
    "        #bagging, bootstrap aggregating\n",
    "        #we randomly select data from the original dataset\n",
    "        #to form a new subset with the same size of the original\n",
    "        #in the new subset, data points are not unique\n",
    "        #some of the data points can occur more than once\n",
    "        #some of the data points from the original may not appear\n",
    "        #we call these outliers out-of-bag\n",
    "        #we can use out-of-bag errors to validate the model\n",
    "        #usually out-of-bag set is approximately 36% of the original\n",
    "        #as for why, you can read it from the link below\n",
    "        #https://towardsdatascience.com/what-is-out-of-bag-oob-score-in-random-forest-a7fa23d710\n",
    "        training=copy.deepcopy(train)\n",
    "        \n",
    "        ind=[rd.choice(training.index.tolist()) for j in range(len(training))]\n",
    "        oob=[i for i in range(len(training)) if i not in ind]\n",
    "        \n",
    "        training=training.loc[ind]\n",
    "        training.reset_index(inplace=True,drop=True)\n",
    "\n",
    "        tree=nx.DiGraph()\n",
    "\n",
    "        guide=factors_split(training,ycol,method)\n",
    "        target=guide['target'][0]\n",
    "        colname=guide['colname'][0]\n",
    "\n",
    "        tree.add_node('0',data=training.index.tolist(),\n",
    "                     target=target,colname=colname)\n",
    "\n",
    "        parentname='0'\n",
    "\n",
    "        build_tree(training,parentname,tree,\n",
    "                   xcol,ycol,method=method,random=True)\n",
    "\n",
    "        #there will be at least ten charts\n",
    "        #so we skip the visualization\n",
    "\n",
    "        #in random forest, there are way too many decision trees\n",
    "        #and each tree may deliver a different classification\n",
    "        #we need to count the vote to get the majority as the prediction\n",
    "        for j in train.index.tolist():\n",
    "            training_forecast.append(predict(tree,'0',train,j))\n",
    "        for j in testing.index.tolist():\n",
    "            testing_forecast.append(predict(tree,'0',testing,j))\n",
    "            \n",
    "\n",
    "        #for each subset we should have one out-of-bag set\n",
    "        #we treat each out-of-bag set as a testing set\n",
    "        #besides traditional train test split\n",
    "        #out-of-bag can be regarded as cross validation here\n",
    "        #if u wanna know why, you can read the paper from this url\n",
    "        # https://www.stat.berkeley.edu/~breiman/OOBestimation.pdf\n",
    "        #we calculate oob score for each subset then take the average\n",
    "        train_oob=train.loc[oob]\n",
    "        train_oob.reset_index(inplace=True,drop=True) \n",
    "        forecast=[]\n",
    "        for j in train_oob.index.tolist():\n",
    "            forecast.append(predict(tree,'0',train_oob,j))\n",
    "        train_oob['projection']=forecast\n",
    "        oob_forecast.append(\n",
    "            len(\n",
    "                train_oob[\n",
    "                    train_oob['y']==train_oob['projection']])/len(train_oob))\n",
    "        \n",
    "\n",
    "    \n",
    "    forecast=[]\n",
    "    for i in range(len(train)):\n",
    "        temp=training_forecast[i::len(train)]\n",
    "        forecast.append(max(set(temp),key=temp.count) )\n",
    "    train['projection']=forecast\n",
    "    \n",
    "    \n",
    "    forecast=[]\n",
    "    for i in range(len(testing)):\n",
    "        temp=testing_forecast[i::len(testing)]\n",
    "        forecast.append(max(set(temp),key=temp.count) )\n",
    "    testing['projection']=forecast\n",
    "    \n",
    "\n",
    "    \n",
    "    print('\\ntrain accuracy: %s'%(\n",
    "        len(train[train['y']==train['projection']])/len(train)))\n",
    "    print('\\ntest accuracy: %s'%(\n",
    "        len(testing[testing['y']==testing['projection']])/len(testing)))\n",
    "    print('\\noob accuracy: %s'%(np.mean(oob_forecast)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sklearm official random forest    \n",
    "def skl_random_forest(train,xcol,ycol):\n",
    "\n",
    "    clf=RandomForestClassifier(oob_score=True).fit(train[xcol],train[ycol])\n",
    "    print('\\ntrain accuracy: %s'%(clf.score(train[xcol],train[ycol])))\n",
    "    print('\\noob accuracy: %s'%(clf.oob_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "train accuracy: 0.9133333333333333\n",
      "\n",
      "test accuracy: 0.9133333333333333\n",
      "\n",
      "oob accuracy: 0.8398042790378091\n"
     ]
    }
   ],
   "source": [
    "random_forest(df,df,xcol,ycol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "train accuracy: 0.9732142857142857\n",
      "\n",
      "test accuracy: 0.8947368421052632\n",
      "\n",
      "oob accuracy: 0.7805706980044235\n"
     ]
    }
   ],
   "source": [
    "random_forest(train,test,xcol,ycol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#due to the randomness in random forest\n",
    "#the instability of homemade random forest is more severe than decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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