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choice_tree.py
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159
choice_tree.py
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from data import tree_format
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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_gini):
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p = float(len(l)) / (len(l) + len(r)) # something like an enthropy
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return current_gini - 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_gini = 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_gini)
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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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281
data.py
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281
data.py
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# get out unique value from each column (dish, temperature or label)
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def uniq_val_from_data(rows, col):
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return set([row[col] for row in rows])
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# format to print a tree and something more
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tree_format = ["dish", "served", "origin", "cooked", "ingredients", "name"]
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# course
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'''
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dish - (salad/meal/coffee/tea/non-alcho drink)
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served - (cold/hot/warm)
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origin - (Worldwide/America/Europe/Asia)
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cooked - (baked/boiled/mixed)
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ingridients - (2/4)
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'''
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training_data = [
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['salad', 'warm', 'Europe', 'mixed', 4, 'Cappon magro'],
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['salad', 'hot', 'Europe', 'mixed', 4, 'Panzanella'],
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['salad', 'cold', 'Europe', 'mixed', 4, 'Greek Salad'],
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['salad', 'warm', 'Worldwide', 'mixed', 4, 'Jello salad'],
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['salad', 'cold', 'Worldwide', 'mixed', 4, 'Macaroni salad'],
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['salad', 'hot', 'Worldwide', 'mixed', 4, 'Fruit salad'],
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['salad', 'cold', 'America', 'mixed', 4, 'Ambrosia Salad'],
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['salad', 'warm', 'America', 'mixed', 4, 'Crab Louie'],
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['salad', 'hot', 'America', 'mixed', 4, 'Taco salad'],
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['salad', 'warm', 'Asia', 'mixed', 4, 'Singju'],
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['salad', 'cold', 'Asia', 'mixed', 4, 'Rojak'],
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['salad', 'hot', 'Asia', 'mixed', 4, 'Shirazi salad'],
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['salad', 'warm', 'Europe', 'mixed', 2, 'Urnebes'],
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['salad', 'hot', 'Europe', 'mixed', 2, 'Shopska salad'],
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['salad', 'cold', 'Europe', 'mixed', 2, 'Wurstsalat'],
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['salad', 'warm', 'Worldwide', 'mixed', 2, 'Garden Salad'],
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['salad', 'cold', 'Worldwide', 'mixed', 2, 'Mesclun'],
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['salad', 'hot', 'Worldwide', 'mixed', 2, 'Egg salad'],
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['salad', 'cold', 'America', 'mixed', 2, 'Watergate salad'],
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['salad', 'warm', 'America', 'mixed', 2, 'Michigan salad'],
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['salad', 'hot', 'America', 'mixed', 2, ''],
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['salad', 'warm', 'Asia', 'mixed', 2, 'Yam thua phu'],
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['salad', 'cold', 'Asia', 'mixed', 2, 'Som tam'],
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['salad', 'hot', 'Asia', 'mixed', 2, 'Yam pla duk fu'],
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['salad', 'warm', 'Europe', 'baked', 4, 'Roasted Pepper Panzanella'],
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['salad', 'hot', 'Europe', 'baked', 4, 'Walnut Salad with Fried Eggs'],
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['salad', 'cold', 'Europe', 'baked', 4, 'Frisée and Wild Mushroom Salad with Poached Egg'],
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['salad', 'warm', 'Worldwide', 'baked', 4, 'Grilled Mushrooms and Carrots with Sesame'],
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['salad', 'cold', 'Worldwide', 'baked', 4, 'Coleslaw'],
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['salad', 'hot', 'Worldwide', 'baked', 4, 'Smashed Potato Salad'],
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['salad', 'cold', 'America', 'baked', 4, 'Wintery Beetroot and Lentil Salad'],
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['salad', 'warm', 'America', 'baked', 4, 'Cookie salad'],
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['salad', 'hot', 'America', 'baked', 4, 'Curtido'],
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['salad', 'warm', 'Asia', 'baked', 4, 'Urap'],
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['salad', 'cold', 'Asia', 'baked', 4, 'Quinoa Salad'],
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['salad', 'hot', 'Asia', 'baked', 4, 'Kosambari'],
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['salad', 'warm', 'Europe', 'baked', 2, 'Wilted Escarole Salad'],
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['salad', 'hot', 'Europe', 'baked', 2, 'Shrimp and Escarole Salad'],
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['salad', 'cold', 'Europe', 'baked', 2, 'Cappon magro'],
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['salad', 'warm', 'Worldwide', 'baked', 2, 'Carrot Salad'],
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['salad', 'cold', 'Worldwide', 'baked', 2, 'Smashed Potato Salad'],
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['salad', 'hot', 'Worldwide', 'baked', 2, 'Bistro Salad with Roasted Vegetables'],
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['salad', 'cold', 'America', 'baked', 2, 'Charred Romanesco with Anchovies and Mint'],
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['salad', 'warm', 'America', 'baked', 2, 'Warm Cauliflower and Herbed Barley Salad'],
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['salad', 'hot', 'America', 'baked', 2, 'Steak Salad with Horseradish Dressing'],
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['salad', 'warm', 'Asia', 'baked', 2, 'Steak Salad with Horseradish Dressing'],
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['salad', 'cold', 'Asia', 'baked', 2, 'Green papaya salad'],
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['salad', 'hot', 'Asia', 'baked', 2, 'Grilled Sesame Shrimp with Herb Salad'],
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['coffee', 'hot', 'Worldwide', 'boiled', 2, 'Espresso'],
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['coffee', 'warm', 'Worldwide', 'boiled', 2, 'Latte'],
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['coffee', 'cold', 'Worldwide', 'boiled', 2, 'Cappuccino'],
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['coffee', 'hot', 'Europe', 'boiled', 2, 'Affogato'],
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['coffee', 'warm', 'Europe', 'boiled', 2, 'Botz'],
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['coffee', 'cold', 'Europe', 'boiled', 2, 'Affogato'],
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['coffee', 'hot', 'America', 'boiled', 2, 'Café de olla'],
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['coffee', 'warm', 'America', 'boiled', 2, 'Double Double Coffee'],
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['coffee', 'cold', 'America', 'boiled', 2, 'Pocillo'],
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['coffee', 'hot', 'Asia', 'boiled', 2, 'Melya'],
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['coffee', 'warm', 'Asia', 'boiled', 2, 'borgia'],
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['coffee', 'cold', 'Asia', 'boiled', 2, 'Kaapi'],
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['coffee', 'hot', 'Worldwide', 'mixed', 2, 'Nescafé'],
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['coffee', 'warm', 'Worldwide', 'mixed', 2, 'Moccona'],
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['coffee', 'cold', 'Worldwide', 'mixed', 2, 'Kenco'],
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['coffee', 'hot', 'Europe', 'mixed', 2, 'Frappé'],
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['coffee', 'warm', 'Europe', 'mixed', 2, 'Marocchino'],
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['coffee', 'cold', 'Europe', 'mixed', 2, 'Shakerato'],
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['coffee', 'hot', 'America', 'mixed', 2, 'Mazagran'],
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['coffee', 'warm', 'America', 'mixed', 2, 'Medici'],
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['coffee', 'cold', 'America', 'mixed', 2, 'Palazzo'],
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['coffee', 'hot', 'Asia', 'mixed', 2, 'Qishr.'],
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['coffee', 'warm', 'Asia', 'mixed', 2, 'Egg Coffee'],
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['coffee', 'cold', 'Asia', 'mixed', 2, 'Yuanyang'],
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['tea', 'warm', 'Asia', 'boiled', 2, 'Bubble Tea'],
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['tea', 'hot', 'Asia', 'boiled', 2, 'White Tea'],
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['tea', 'cold', 'Asia', 'boiled', 2, 'Pu Erh'],
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['tea', 'warm', 'Asia', 'boiled', 4, 'Hong Kong-Style Milk Tea'],
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['tea', 'hot', 'Asia', 'boiled', 4, 'Darjeeling'],
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['tea', 'cold', 'Asia', 'boiled', 4, 'Butter Tea'],
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['tea', 'warm', 'Europe', 'boiled', 2, 'Earl Grey'],
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['tea', 'hot', 'Europe', 'boiled', 2, 'Wild Lily Tea'],
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['tea', 'cold', 'Europe', 'boiled', 2, 'Chamomilla Bohemica'],
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['tea', 'warm', 'America', 'boiled', 2, 'Argo Tea'],
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['tea', 'hot', 'America', 'boiled', 2, 'Bigelow Tea'],
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['tea', 'cold', 'America', 'boiled', 2, 'American Tea'],
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['tea', 'warm', 'Worldwide', 'boiled', 2, 'Yellow tea'],
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['tea', 'hot', 'Worldwide', 'boiled', 2, 'Mulberry black tea'],
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['tea', 'cold', 'Worldwide', 'boiled', 2, 'Chai'],
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['non-alcho drink', 'warm', 'Worldwide', 'mixed', 2, 'Lager'],
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['non-alcho drink', 'hot', 'Worldwide', 'mixed', 2, 'Chocoart'],
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['non-alcho drink', 'cold', 'Worldwide', 'mixed', 2, 'Pucko'],
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['non-alcho drink', 'warm', 'Europe', 'mixed', 2, 'Pinolillo'],
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['non-alcho drink', 'hot', 'Europe', 'mixed', 2, 'Pópo'],
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['non-alcho drink', 'cold', 'Europe', 'mixed', 2, 'Pozol'],
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['non-alcho drink', 'warm', 'Asia', 'mixed', 2, 'Milo'],
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['non-alcho drink', 'hot', 'Asia', 'mixed', 2, 'Tejate'],
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['non-alcho drink', 'cold', 'Asia', 'mixed', 2, 'Soju'],
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['non-alcho drink', 'warm', 'America', 'mixed', 2, 'Xicolatada'],
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['non-alcho drink', 'hot', 'America', 'mixed', 2, 'Swiss Miss'],
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['non-alcho drink', 'cold', 'America', 'mixed', 2, 'Mate'],
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['non-alcho drink', 'warm', 'Worldwide', 'boiled', 2, 'Barley water'],
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['non-alcho drink', 'hot', 'Worldwide', 'boiled', 2, 'Egg cream'],
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['non-alcho drink', 'cold', 'Worldwide', 'boiled', 2, 'Mulled apple juice'],
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['non-alcho drink', 'warm', 'Europe', 'boiled', 2, 'Cola Cao'],
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['non-alcho drink', 'hot', 'Europe', 'boiled', 2, 'Kókómjólk'],
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['non-alcho drink', 'cold', 'Europe', 'boiled', 2, 'Tascalate'],
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['non-alcho drink', 'warm', 'Asia', 'boiled', 2, 'Choc-Ola'],
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['non-alcho drink', 'hot', 'Asia', 'boiled', 2, 'Akta-Vite'],
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['non-alcho drink', 'cold', 'Asia', 'boiled', 2, 'Banania'],
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['non-alcho drink', 'warm', 'America', 'boiled', 2, 'Caipirinha'],
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['non-alcho drink', 'hot', 'America', 'boiled', 2, 'Pisco sour'],
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['non-alcho drink', 'cold', 'America', 'boiled', 2, 'Rum swizzle'],
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['meal', 'warm', 'Worldwide', 'mixed', 2, 'Lasagna'],
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['meal', 'hot', 'Worldwide', 'mixed', 2, 'Chicken Pot Pie'],
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['meal', 'cold', 'Worldwide', 'mixed', 2, 'Smothered Pork Chops'],
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['meal', 'warm', 'Europe', 'mixed', 2, 'Gumbo'],
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['meal', 'hot', 'Europe', 'mixed', 2, 'Chicken Tortilla Soup'],
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['meal', 'cold', 'Europe', 'mixed', 2, 'Potato Pinwheels'],
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['meal', 'warm', 'Asia', 'mixed', 2, 'Tex-Mex'],
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['meal', 'hot', 'Asia', 'mixed', 2, 'Manti'],
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['meal', 'cold', 'Asia', 'mixed', 2, 'Khichdi'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'America', 'mixed', 2, 'Kansas City-style barbecue'],
|
||||||
|
['meal', 'hot', 'America', 'mixed', 2, 'Barbecue in Texas'],
|
||||||
|
['meal', 'cold', 'America', 'mixed', 2, 'Sloppy joe'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Worldwide', 'boiled', 2, 'Hot dog'],
|
||||||
|
['meal', 'hot', 'Worldwide', 'boiled', 2, 'Pesto Boiled Potatoes'],
|
||||||
|
['meal', 'cold', 'Worldwide', 'boiled', 2, 'Spinach Soup'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Europe', 'boiled', 2, 'Jambalaya'],
|
||||||
|
['meal', 'hot', 'Europe', 'boiled', 2, 'Black Chickpeas'],
|
||||||
|
['meal', 'cold', 'Europe', 'boiled', 2, 'Vegetable Soup'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Asia', 'boiled', 2, 'Gumbo'],
|
||||||
|
['meal', 'hot', 'Asia', 'boiled', 2, 'Dirty Rice'],
|
||||||
|
['meal', 'cold', 'Asia', 'boiled', 2, 'Hawaiian haystack'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'America', 'boiled', 2, 'Goetta'],
|
||||||
|
['meal', 'hot', 'America', 'boiled', 2, 'Chaudin'],
|
||||||
|
['meal', 'cold', 'America', 'boiled', 2, 'Goetta'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Worldwide', 'baked', 2, 'Chicken Curry'],
|
||||||
|
['meal', 'hot', 'Worldwide', 'baked', 2, 'Fugazza'],
|
||||||
|
['meal', 'cold', 'Worldwide', 'baked', 2, 'Halloumi and watermelon'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Europe', 'baked', 2, 'Moussaka'],
|
||||||
|
['meal', 'hot', 'Europe', 'baked', 2, 'Köttbullar'],
|
||||||
|
['meal', 'cold', 'Europe', 'baked', 2, 'Haggis'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Asia', 'baked', 2, 'Hainanese Chicken Rice'],
|
||||||
|
['meal', 'hot', 'Asia', 'baked', 2, 'Chicken bog'],
|
||||||
|
['meal', 'cold', 'Asia', 'baked', 2, 'Yeung Chow fried rice'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'America', 'baked', 2, 'Mexican pizza'],
|
||||||
|
['meal', 'hot', 'America', 'baked', 2, 'California-style pizza'],
|
||||||
|
['meal', 'cold', 'America', 'baked', 2, 'Chocolate pizza'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Worldwide', 'mixed', 4, 'Pizza cake'],
|
||||||
|
['meal', 'hot', 'Worldwide', 'mixed', 4, 'Pan Pizza'],
|
||||||
|
['meal', 'cold', 'Worldwide', 'mixed', 4, 'Neapolitan pizza'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Europe', 'mixed', 4, 'Palatschinken'],
|
||||||
|
['meal', 'hot', 'Europe', 'mixed', 4, 'Currywurst'],
|
||||||
|
['meal', 'cold', 'Europe', 'mixed', 4, 'Potica'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Asia', 'mixed', 4, 'Sushi'],
|
||||||
|
['meal', 'hot', 'Asia', 'mixed', 4, 'Satay'],
|
||||||
|
['meal', 'cold', 'Asia', 'mixed', 4, 'Laksa'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'America', 'mixed', 4, 'Simple Shepherd’s Pie'],
|
||||||
|
['meal', 'hot', 'America', 'mixed', 4, 'Apple Pie'],
|
||||||
|
['meal', 'cold', 'America', 'mixed', 4, 'American burnt onion dip'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Worldwide', 'boiled', 4, 'Fries'],
|
||||||
|
['meal', 'hot', 'Worldwide', 'boiled', 4, 'Cheese fondue'],
|
||||||
|
['meal', 'cold', 'Worldwide', 'boiled', 4, 'Goulash'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Europe', 'boiled', 4, 'Arancini'],
|
||||||
|
['meal', 'hot', 'Europe', 'boiled', 4, 'Pierogi'],
|
||||||
|
['meal', 'cold', 'Europe', 'boiled', 4, 'Waffles'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Asia', 'boiled', 4, 'Tom Yum'],
|
||||||
|
['meal', 'hot', 'Asia', 'boiled', 4, 'Calas'],
|
||||||
|
['meal', 'cold', 'Asia', 'boiled', 4, 'Dim Sum'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'America', 'boiled', 4, 'Glorified rice'],
|
||||||
|
['meal', 'hot', 'America', 'boiled', 4, 'Hominy Grits'],
|
||||||
|
['meal', 'cold', 'America', 'boiled', 4, 'Spring Rolls'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Worldwide', 'baked', 4, 'Fish and Chips'],
|
||||||
|
['meal', 'hot', 'Worldwide', 'baked', 4, 'Fried Rice'],
|
||||||
|
['meal', 'cold', 'Worldwide', 'baked', 4, 'Black Bean Burger'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Europe', 'baked', 4, 'Sweet Potato Pasta Bake'],
|
||||||
|
['meal', 'hot', 'Europe', 'baked', 4, 'Oven-Baked Meatballs'],
|
||||||
|
['meal', 'cold', 'Europe', 'baked', 4, 'Sheet-Pan Greek Chicken and Veggies'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'Asia', 'baked', 4, 'Fish Balls'],
|
||||||
|
['meal', 'hot', 'Asia', 'baked', 4, 'Thai Coconut Braised Chicken and Potatoes'],
|
||||||
|
['meal', 'cold', 'Asia', 'baked', 4, 'Teriyaki Tofu and Broccoli'],
|
||||||
|
|
||||||
|
['meal', 'warm', 'America', 'baked', 4, 'Pecan pie with maple cream'],
|
||||||
|
['meal', 'hot', 'America', 'baked', 4, 'Breaded Chicken Spinach Burgers'],
|
||||||
|
['meal', 'cold', 'America', 'baked', 4, 'Oven-Baked Fajitas'],
|
||||||
|
]
|
||||||
|
|
||||||
|
dish = uniq_val_from_data(training_data, 0)
|
||||||
|
served = uniq_val_from_data(training_data, 1)
|
||||||
|
origin = uniq_val_from_data(training_data, 2)
|
||||||
|
cooked = uniq_val_from_data(training_data, 3)
|
||||||
|
ingredients = uniq_val_from_data(training_data, 4)
|
||||||
|
|
||||||
|
# We can also use this function instead of the direct appending to the list
|
||||||
|
'''
|
||||||
|
rand_data = []
|
||||||
|
|
||||||
|
for each in range(0, len(training_data)-1):
|
||||||
|
rand_data.append(uniq_val_from_data(training_data, each))
|
||||||
|
'''
|
||||||
|
|
||||||
|
rand_data = [dish, served, origin, cooked, ingredients]
|
||||||
|
|
||||||
|
#print(len(training_data))
|
||||||
Loading…
Reference in New Issue
Block a user