185 lines
4.6 KiB
Python
185 lines
4.6 KiB
Python
import data
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training_data = data.learning_data
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header = ['color', 'shape', 'weight', 'size', 'name']
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# funkcja która zwraca listę unikalnych wartości z każdej kolumny
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def uniqie_vals(rows, col):
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return set([row[col] for row in rows])
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# zliczamy liczbę wystąpień danego typu w zestawie danych
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def class_counts(rows):
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counts = {} # label -> count
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for row in rows:
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name = row[-1]
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if name not in counts:
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counts[name] = 0
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counts[name] += 1
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return counts
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# funkcja do sprawdzania czy wartość jest wartością numeryczną
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def is_numeric(val):
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return isinstance(val, int) or isinstance(val, float)
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# klasa do zadawania pytań
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class Question:
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def __init__(self, column, value):
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self.column = column
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self.value = value
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def match(self, example):
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val = example[self.column]
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if is_numeric(val):
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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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condition = '=='
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if is_numeric(self.value):
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condition = '>='
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return "Is %s %s %s?" % (header[self.column], condition, str(self.value))
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def partition(rows, question):
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""" podział zbioru informacji
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dla każdego rzędu w zbiorze, sprawdź czy zgadza się z pytaniem, jeśli tak
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dodaj do 'true' inaczej dodaj do 'false' """
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true_rows, false_rows = [], []
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for row in rows:
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if question.match(row):
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true_rows.append(row)
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else:
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false_rows.append(row)
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return true_rows, false_rows
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def gini(rows):
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""" Gini impurity is a measure of how often a randomly chosen element from
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the set would be incorrectly labeled if it was randomly labeled according to
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the distribution of labels in the subset. """
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counts = class_counts(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(left, right, current_uncertainty):
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p = float(len(left)) / (len(left) + len(right))
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return current_uncertainty - p * gini(left) - (1 - p) * gini(right)
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def find_best_split(rows):
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""" znajdź najlepsze możliwe pytanie do zadania, sprawdzając wszystkie
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właściwośći oraz licząc dla nich 'info_gain' """
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best_gain = 0
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best_question = None
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current_uncertainty = gini(rows)
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n_features = len(rows[0]) - 1
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for col in range(n_features):
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values = set([row[col] for row in rows])
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for val in values:
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question = Question(col, val)
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true_rows, false_rows = partition(rows, question)
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if len(true_rows) == 0 or len(false_rows) == 0:
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continue
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gain = info_gain(true_rows, false_rows, current_uncertainty)
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if gain > best_gain:
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best_gain, best_question = gain, question
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return best_gain, best_question
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class Leaf:
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def __init__(self, rows):
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self.predicions = class_counts(rows)
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class DecisionNode:
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def __init__(self, question, true_branch, false_branch):
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self.question = question
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self.true_branch = true_branch
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self.false_branch = false_branch
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def build_tree(rows):
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gain, question = find_best_split(rows)
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if gain == 0:
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return Leaf(rows)
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true_rows, false_rows = partition(rows, question)
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true_branch = build_tree(true_rows)
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false_branch = build_tree(false_rows)
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return DecisionNode(question, true_branch, false_branch)
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def print_tree(node, spacing=""):
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if isinstance(node, Leaf):
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print(spacing + "Predict", node.predicions)
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else:
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print(spacing + str(node.question))
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print(spacing + '--> True:')
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print_tree(node.true_branch, spacing + " ")
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print(spacing + '--> False:')
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print_tree(node.false_branch, spacing + " ")
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def classify(row, node):
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if isinstance(node, Leaf):
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return node.predicions
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if node.question.match(row):
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return classify(row, node.true_branch)
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else:
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return classify(row, node.false_branch)
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def print_leaf(counts):
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probs = []
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for lbl in counts.keys():
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probs.append(lbl)
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return probs
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# my_tree = build_tree(training_data)
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#
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# print_tree(my_tree)
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#
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# testing_data = [
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# ['gold', 'rectangle', 50, 'medium', 'Name'],
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# ['brown', 'rectangle', 55, 'medium', 'Snickers'],
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# ['white', 'rectangle', 120, 'big', 'Name']
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# ]
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#
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# test = ['white', 'rectangle', 120, 'big', 'Name']
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#
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# # for row in testing_data:
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# # print(print_leaf(classify(row, my_tree)))
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#
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# wynik = print_leaf(classify(test, my_tree))[0]
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# print(wynik)
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