treepredict.py for Ch7 Programming Collective Intelligence

from PIL import Image,ImageDraw

my_data=[line.split('\t') for line in file('decision_tree_example.txt')]
# Replace '/n' or it taints the data (example:{'Basic\n': 4, 'Premium\n': 1, 'Basic': 1})
for a in range(len(my_data)):
    for b in range(len(my_data[a])):
        my_data[a][b] = my_data[a][b].replace('\n','')


class decisionnode:
    def __init__(self,col=-1,value=None,results=None,tb=None,fb=None):
        self.col=col
        self.value=value
        self.results=results
        self.tb=tb
        self.fb=fb

# Divides a set on a specific column. Can handle numeric or nominal values
def divideset(rows,column,value):
    try: value = int(value) # The numerical data is saved as strings within my_data
    except: pass

    # Make a function that tells us if a row is in the first group (true) or the second (false)
    split_function=None
    if isinstance(value,int) or isinstance(value,float):
        split_function=lambda row: int(row[column])>=value
    else:
        split_function=lambda row:row[column]==value

    # Divide the rows into two sets and return them
    set1=[row for row in rows if split_function(row)]
    set2=[row for row in rows if not split_function(row)]
    return (set1,set2)

# Create counts of possible results ( the last column of each row is the result)
def uniquecounts(rows):
    results={}
    for row in rows:
        # The result is the last column
        r=row[len(row)-1]
        if r not in results: results[r]=0
        results[r]+=1
    return results

# Probability that a randomly placed item will be in the wrong category
def giniimpurity(rows):
    total=len(rows)
    counts=uniquecounts(rows)
    imp=0
    for k1 in counts:
        p1=float(counts[k1])/total
        for k2 in counts:
            if k1==k2: continue
            p2=float(counts[k2])/total
            imp+=p1*p2
    return imp

# Entropy is the sum of p(x)log(p(x)) across all the different possible results
def entropy(rows):
    from math import log
    log2=lambda x:log(x)/log(2)
    results=uniquecounts(rows)
    # Now calculate the entropy
    ent=0.0
    for r in results.keys():
        p=float(results[r])/len(rows)
        ent=ent-p*log2(p)
    return ent

def buildtree(rows,scoref=entropy):
    if len(rows)==0: return decisionnode()
    current_score=scoref(rows)

    # set up some variables to track the best criteria
    best_gain=0.0
    best_criteria=None
    best_sets=None

    column_count=len(rows[0])-1
    for col in range(0,column_count):
        # Generate the list of different values in this column
        column_values={}
        for row in rows:
            column_values[row[col]]=1
        # Now try dividing the rows up for each value in this column
        for value in column_values.keys():
            (set1,set2)=divideset(rows,col,value)

            # Information gain
            p=float(len(set1))/len(rows)
            gain=current_score-p*scoref(set1)-(1-p)*scoref(set2)
            if gain>best_gain and len(set1)>0 and len(set2)>0:
                best_gain=gain
                best_criteria=(col,value)
                best_sets=(set1,set2)
    # Create the subbranches
    if best_gain>0:
        trueBranch=buildtree(best_sets[0],scoref)                        # corrected adding scoref
        falseBranch=buildtree(best_sets[1],scoref)                       # corrected adding scoref
        return decisionnode(col=best_criteria[0],value=best_criteria[1],
                            tb=trueBranch,fb=falseBranch)
    else:
        return decisionnode(results=uniquecounts(rows))

def printtree(tree,indent='  '):
    # Is this a leaf node?
    if tree.results!=None:
        print str(tree.results)
    else:
        # Print the criteria
        print str(tree.col)+':'+str(tree.value)+'? '

        # Print the branches
        print indent+'T->',
        printtree(tree.tb,indent+'  ')
        print indent+'F->',
        printtree(tree.fb,indent+'  ')

def getwidth(tree):
    if tree.tb==None and tree.fb==None: return 1
    return getwidth(tree.tb)+getwidth(tree.fb)

def getdepth(tree):
    if tree.tb==None and tree.fb==None: return 0
    return max(getdepth(tree.tb),getdepth(tree.fb))+1

def drawtree(tree,jpeg='tree.jpg'):
    w=getwidth(tree)*100
    h=getdepth(tree)*100+120

    img=Image.new('RGB',(w,h),(255,255,255))
    draw=ImageDraw.Draw(img)

    drawnode(draw,tree,w/2,20)
    img.save(jpeg,'JPEG')

def drawnode(draw,tree,x,y):
    if tree.results==None:
        # Get the width of each branch
        w1=getwidth(tree.fb)*100
        w2=getwidth(tree.tb)*100

        # Determine the total space required by this node
        left=x-(w1+w2)/2
        right=x+(w1+w2)/2

        # Draw the condition string
        draw.text((x-20,y-10),str(tree.col)+':'+str(tree.value),(0,0,0))

        # Draw links to the branches
        draw.line((x,y,left+w1/2,y+100),fill=(255,0,0))
        draw.line((x,y,right-w2/2,y+100),fill=(255,0,0))

        # Draw the branch nodes
        drawnode(draw,tree.fb,left+w1/2,y+100)
        drawnode(draw,tree.tb,right-w2/2,y+100)
    else:
        txt=' \n'.join(['%s:%d'%v for v in tree.results.items()])
        draw.text((x-20,y),txt,(0,0,0))

def classify(observation,tree):
    if tree.results!=None:
        return tree.results
    else:
        v=observation[tree.col]
        branch=None
        if isinstance(v,int) or isinstance(v,float):
            if v>=tree.value: branch=tree.tb
            else: branch=tree.fb
        else:
            if v==tree.value: branch=tree.tb
            else: branch=tree.fb
        return classify(observation,branch)

def prune(tree,mingain):
    # If the branches aren't leaves prune them
    if tree.tb.results==None:
        prune(tree.tb,mingain)
    if tree.fb.results==None:
        prune(tree.fb,mingain)

    # If both the subbranches are now leaves, see if they should be merged
    if tree.tb.results!=None and tree.fb.results!=None:
        # Build a combined dataset
        tb,fb=[],[]
        for v,c in tree.tb.results.items():
            tb+=[[v]]*c
        for v,c in tree.fb.results.items():
            fb+=[[v]]*c

        # Test the reduction in entropy
        delta=entropy(tb+fb)-(entropy(tb)+entropy(fb))/2     # Xiaochen Wang CORRECTION
        if delta<mingain:
            # Merge the branches
            tree.tb,tree.fb=None,None
            tree.results=uniquecounts(tb+fb)

def mdclassify(observation,tree):
    if tree.results!=None:
        return tree.results
    else:
        v=observation[tree.col]
        if v==None:
            tr,fr=mdclassify(observation,tree.tb),mdclassify(observation,tree.fb)
            tcount=sum(tr.values())
            fcount=sum(fr.values())
            tw=float(tcount)/(tcount+fcount)
            fw=float(fcount)/(tcount+fcount)
            result={}
            for k,v in tr.items(): result[k]=v*tw
            for k,v in fr.items(): result[k]=v*fw
            return result
        else:
            if isinstance(v,int) or isinstance(v,float):
                if v>tree.value: branch=tree.tb
                else: branch=tree.fb
            else:
                if v==tree.value: branch=tree.tb
                else: branch=tree.fb
            return mdclassify(observation,branch)

def variance(rows):
    if len(rows)==0: return 0
    data=[float(row[len(row)-1]) for row in rows]
    mean=sum(data)/len(data)
    variance=sum([(d-mean)**2 for d in data])/len(data)
    return variance