decision_tree_lab1_ex

# -*- coding: utf-8 -*-

"""
Да се промени класата за дрво на одлука да чува и информација на кое ниво во дрвото се наоѓа јазолот.
Потоа да се променат и функциите за градење и печатење на дрвото така што за секој јазол ќе се печати и нивото.
Коренот е на нулто ниво. На излез со функцијата printTree треба да се испечати даденото тренинг множество.
Прочитана инстанца од стандарден влез да се додаде на тренинг множеството и потоа да се истренира и испечати истото.
"""

trainingData=[['slashdot','USA','yes',18,'None'],
        ['google','France','yes',23,'Premium'],
        ['google','France','yes',23,'Basic'],
        ['google','France','yes',23,'Basic'],
        ['digg','USA','yes',24,'Basic'],
        ['kiwitobes','France','yes',23,'Basic'],
        ['google','UK','no',21,'Premium'],
        ['(direct)','New Zealand','no',12,'None'],
        ['(direct)','UK','no',21,'Basic'],
        ['google','USA','no',24,'Premium'],
        ['slashdot','France','yes',19,'None'],
        ['digg','USA','no',18,'None'],
        ['google','UK','no',18,'None'],
        ['kiwitobes','UK','no',19,'None'],
        ['digg','New Zealand','yes',12,'Basic'],
        ['slashdot','UK','no',21,'None'],
        ['google','UK','yes',18,'Basic'],
        ['kiwitobes','France','yes',19,'Basic']]

class decisionnode:
    def __init__(self, col=-1, value=None, results=None, tb=None, fb=None, lvl=0):
        self.col = col
        self.value = value
        self.results = results
        self.tb = tb
        self.fb = fb
        self.lvl = lvl

def sporedi_broj(row, column, value):
    return row[column] >= value

def sporedi_string(row, column, value):
    return row[column] == value

def divideset(rows, column, value):
    split_function = None
    # print(split_function)
    if isinstance(value, int) or isinstance(value, float):
        split_function = sporedi_broj
    else:
        split_function = sporedi_string
    set_false = []
    set_true = []
    for row in rows:
        if split_function(row, column, value):
            set_true.append(row)
        else:
            set_false.append(row)
    # print(len(set_true),len(set_false))
    set1 = [row for row in rows if split_function(row, column, value)]  # za sekoj row od rows za koj split_function vrakja true
    set2 = [row for row in rows if not split_function(row, column, value)]  # za sekoj row od rows za koj split_function vrakja false

    return (set_true, set_false)

def uniquecounts(rows):
    results = {}
    for row in rows:
        # The result is the last column
        r = row[-1]
        results.setdefault(r, 0)
        results[r] += 1
    return results

def log2(x):
    from math import log
    l2 = log(x) / log(2)
    return l2

# Entropy is the sum of p(x)log(p(x)) across all
# the different possible results
def entropy(rows):
    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, l=-1):
    if len(rows) == 0: return decisionnode()
    current_score = scoref(rows)
    # Set up some variables to track the best criteria
    best_gain = 0.0
    best_column = -1
    best_value = None
    best_subsetf = None
    best_subsett = None
    column_count = len(rows[0]) - 1
    for col in range(column_count):
        # Generate the list of different values in
        # this column
        column_values = set()
        for row in rows:
            column_values.add(row[col])
        # Now try dividing the rows up for each value
        # in this column
        for value in column_values:
            (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_column = col
                #best_value = value
                #best_subsett = set1
                #best_subsetf = set2
                best_criteria = (col, value)
                best_sets = (set1, set2)

    # Create the subbranches
    if best_gain > 0:
        l+=1
        trueBranch = buildtree(best_sets[0], scoref, l=l)
        falseBranch = buildtree(best_sets[1], scoref, l=l)
        return decisionnode(col=best_criteria[0], value=best_criteria[1],
                            tb=trueBranch, fb=falseBranch, lvl=l)
    else:
        return decisionnode(results=uniquecounts(rows))


def printtree(tree, indent=''):
    # Is this a leaf node?
    if tree.results != None:
        print(tree.results)
    else:
        # Print the criteria
        lvl = tree.lvl
        print(str(tree.col) + ':' + str(tree.value) + '? '+'Level='+str(tree.lvl))
        # Print the branches
        print(indent + 'T->'),
        printtree(tree.tb, indent + '  ')
        print(indent + 'F->'),
        printtree(tree.fb, indent + '  ')


def classify(observation, tree):
    if tree.results != None:
        return tree.results
    else:
        vrednost = observation[tree.col]
        branch = None

        if isinstance(vrednost, int) or isinstance(vrednost, float):
            if vrednost >= tree.value:
                branch = tree.tb
            else:
                branch = tree.fb
        else:
            if vrednost == tree.value:
                branch = tree.tb
            else:
                branch = tree.fb

        return classify(observation, branch)

if __name__ == '__main__':

    referrer = 'google'
    location = 'UK'
    readFAQ = 'no',
    pagesVisited = 18
    serviceChosen = 'None'

    tmp = [referrer, location, readFAQ, pagesVisited, serviceChosen]
    trainingData.append(tmp)

    t = buildtree(trainingData)

    printtree(t)