moved tree
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main.py
203
main.py
@ -7,6 +7,8 @@ import pygad
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from concepts import *
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from graphviz import *
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import numpy as np
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from data import *
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from choice_tree import *
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pygame.init()
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@ -28,198 +30,23 @@ display = pygame.display.set_mode((WIDTH * 32 + 200, HEIGHT * 32))
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# eating time
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EAT_TIME = 15
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#### Menu
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menu = Context.fromstring(''' |meat|salad|meal|drink|cold|hot |
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Pork | X | | | | | X |
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Espresso | | | | X | | X |
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Green Tea | | | | X | X | |
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Greek Salad| | X | | | X | |
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Pizza | | | X | | | X |''')
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training_data = [
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['meat','hot','Pork'],
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['salad','cold','Greek Salad'],
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['drink','hot','Espresso'],
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['drink','cold','Green Tea'],
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['meal','hot','Pizza'],
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]
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tree_format = ["dish", "temperature", "label"]
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#menu.lattice.graphviz()
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#Digraph.render('Lattice.gv', view=True)
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#print(menu.extension(['meal',]))
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#print(func_output)
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def uniq_count(rows):
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#count uniq labels(names)
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count = {}
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for row in rows:
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lbl = row[-1]
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if lbl not in count:
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count[lbl] = 0
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count[lbl] += 1
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return count
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#didn't used
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def isnumer(val):
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return isinstance(val, int) or isinstance(val, float)
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class Question():
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def __init__(self, col, value):
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self.col = col #column
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self.value = value #value of column
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def compare(self, example):
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#compare val in example with val in the question
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val = example[self.col]
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if isnumer(val): #in case menu have prices
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return val >= self.value
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else:
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return val == self.value
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def __repr__(self):
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#just to print
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condition = "=="
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if isnumer(self.value):
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condition = ">="
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return "Is %s %s %s?" % (tree_format[self.col], condition, str(self.value))
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def split(rows, quest):
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#split data into True and False
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t_rows, f_rows = [], []
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for row in rows:
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if quest.compare(row):
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t_rows.append(row)
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else:
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f_rows.append(row)
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return t_rows, f_rows
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def gini(rows):
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counts = uniq_count(rows)
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impurity = 1
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for lbl in counts:
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prob_of_lbl = counts[lbl] / float(len(rows))
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impurity -= prob_of_lbl**2
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return impurity
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def info_gain(l, r, current_uncertainty):
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p = float(len(l)) / (len(l) + len(r)) #something like an enthropy?
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return current_uncertainty - p*gini(l) - (1-p)*gini(r)
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def find_best_q(rows):
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#best question to split the data
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best_gain = 0
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best_quest = None
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current_uncertainty = gini(rows)
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n_feat = len(rows[0]) - 1
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for col in range(n_feat):
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vals = set([row[col] for row in rows])
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for val in vals:
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quest = Question(col, val)
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t_rows, f_rows = split(rows, quest)
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if len(t_rows) == 0 or len(f_rows) == 0:
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continue
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gain = info_gain(t_rows, f_rows, current_uncertainty)
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if gain >= best_gain:
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best_gain, best_quest = gain, quest
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return best_gain, best_quest
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class Leaf:
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#contain a number of how many times the label has appeared in dataset
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def __init__(self, rows):
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self.predicts = uniq_count(rows)
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class Decision_Node():
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#contain the question and child nodes
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def __init__(self, quest, t_branch, f_branch):
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self.quest = quest
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self.t_branch = t_branch
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self.f_branch = f_branch
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def build_tree(rows):
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#use info gain and question
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gain, quest = find_best_q(rows)
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#no gain = no more question, so return a Leaf
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if gain == 0:
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return Leaf(rows)
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#split into true and false branch
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t_rows, f_rows = split(rows, quest)
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#print out branches
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t_branch = build_tree(t_rows)
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f_branch = build_tree(f_rows)
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#return the child/leaf
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return Decision_Node(quest, t_branch, f_branch)
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def print_tree(node, spc=""):
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#if node is a leaf
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if isinstance(node, Leaf):
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print(" " + "Predict", node.predicts)
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return #end of function
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#Or question
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print("" + str(node.quest))
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#True branch
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print("" + '--> True:')
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print_tree(node.t_branch, spc + " ")
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#False branch
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print("" + '--> False:')
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print_tree(node.f_branch, spc + " ")
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def classify(row, node):
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#return our prediction in case the node is a leaf
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if isinstance(node, Leaf):
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return node.predicts
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#otherwise go to the child
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if node.quest.compare(row):
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return classify(row, node.t_branch)
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else:
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return classify(row, node.f_branch)
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def print_leaf(counts):
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#count prediction
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total = sum(counts.values())*1.0
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probs = {} #probability
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for lbl in counts.keys():
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probs[lbl] = str(int(counts[lbl] / total*100)) + "%"
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return probs
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#print(menu.extension(['meal',]))
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tree = build_tree(training_data)
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#order_len = len(tree_format)
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print_tree(tree)
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def client_ordering():
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order = []
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for i in range(0, len(tree_format)-1):
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tmpr = random.sample(rand_data[i], 1)
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order.append(tmpr[0])
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order.append('order')
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return order
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###
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###
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class Node:
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def __init__(self, state, parent, action):
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