preparing new evaluation method
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Jonas Weinz
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006bf59eb0
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@ -659,6 +659,9 @@ class RecipeTreeNode(object):
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return r
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def serialize_subtree(self):
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return [n.serialize() for n in self.traverse()]
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def node_score(self):
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raise NotImplementedError()
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@ -781,8 +784,8 @@ class MixNode(RecipeTreeNode):
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#if c_grouped_mix[ia,ib] > 0 or c_grouped_mix[ib,ia] > 0:
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# s += 1
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ia = m_mix._label_index[grouped_ing_a.to_json()]
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ib = m_mix._label_index[grouped_ing_b.to_json()]
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ia = m_mix._label_index[ing_a.to_json()]
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ib = m_mix._label_index[ing_b.to_json()]
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if c_mix[ia,ib] > 0 or c_mix[ib,ia] > 0:
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s += 1
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@ -1290,10 +1293,10 @@ class Tree(object):
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if node.name() in constant_ingredients:
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node._constant = True
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return Tree(root)
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return Tree(root, main_ingredients)
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@staticmethod
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def from_serialization(s):
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def from_serialization(s, main_ingredients = None):
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def empty_node(raw_n):
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if raw_n['type'] == "MixNode":
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node = MixNode(raw_n['constant'])
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@ -1317,7 +1320,7 @@ class Tree(object):
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for c in childs:
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nodes[id].add_child(nodes[c])
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return Tree(nodes[s[0]['id']])
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return Tree(nodes[s[0]['id']], main_ingredients)
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def __init__(self, root, main_ingredients=None):
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@ -1354,6 +1357,7 @@ class Tree(object):
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def collect_scores(self):
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self._mix_scores = []
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self._mix_weights = []
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self._act_scores = []
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self._ing_scores = []
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@ -1366,6 +1370,7 @@ class Tree(object):
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for n in nodes:
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if type(n) == MixNode:
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self._mix_scores.append(n.node_score())
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self._mix_weights.append(len(n.childs()))
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self._n_mix_nodes += 1
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if type(n) == ActionNode:
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self._act_scores.append(n.node_score())
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@ -1385,12 +1390,13 @@ class Tree(object):
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seen_ingredients.add(n.name())
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self._mix_scores = np.array(self._mix_scores)
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self._mix_weights = np.array(self._mix_weights)
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self._act_scores = np.array(self._act_scores)
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self._ing_scores = np.array(self._ing_scores)
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def mix_scores(self):
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return self._mix_scores
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return self._mix_scores, self._mix_weights
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def action_scores(self):
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return self._act_scores
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@ -1426,11 +1432,11 @@ class Tree(object):
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return self._score
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self.collect_scores()
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s_mix = self.mix_scores()
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s_mix, s_mix_weights = self.mix_scores()
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s_act = self.action_scores()
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s_ing = self.ing_scores()
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n = len(s_mix) + len(s_act) + len(s_ing)
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#n = len(s_mix) + len(s_act) + len(s_ing)
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avg_mix = np.average(s_mix) if len(s_mix) > 0 else 1
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avg_act = np.average(s_act) if len(s_act) > 0 else 1
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@ -1441,21 +1447,34 @@ class Tree(object):
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sum_ing = np.sum(s_ing) if len(s_ing) > 0 else 0
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self._touched = False
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contains_main_ingred = True
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base_main_ings = [i._base_ingredient for i in self.root().traverse_ingredients()]
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for ing in self._main_ingredients:
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if ing not in base_main_ings:
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contains_main_ingred = False
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self._score = 0
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break
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if contains_main_ingred:
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# boost creativity
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if len(s_act) < 3:
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self._score = 0
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elif len(s_ing) < 3:
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self._score = 0
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else:
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self._score = (sum_mix + sum_act + sum_ing) / n
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weighted_mix_score = np.array([s_mix[i] * s_mix_weights[i] for i in range(len(s_mix))])
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#print(weighted_mix_score)
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n = len(s_act) + len(s_ing) + np.sum(s_mix_weights)
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self._score = (np.sum(weighted_mix_score) + sum_act + sum_ing) / n
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#print(self._score)
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self._score *= (len(s_ing) - self._n_duplicates) / len(s_ing)
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#self._score = 0.95 * self._score + 0.05 * self.main_ingredient_score()
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return self._score
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def copy(self):
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return Tree.from_serialization(self.serialize())
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return Tree.from_serialization(self.serialize(), self._main_ingredients)
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# ## Population
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@ -1494,8 +1513,59 @@ class Population(object):
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self.population = new_population
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def crossover(self):
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# shuffle indices
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indices = list(range(len(self.population) // 2))
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indices = [i + len(self.population) // 2 for i in indices]
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random.shuffle(indices)
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# perform crossover for random pairs
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for i in range(len(self.population) // 4):
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i_a = indices[2*i]
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i_b = indices[2*i+1]
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self.pairwise_crossover(self.population[i_a], self.population[i_b])
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def pairwise_crossover(self, tree_a, tree_b):
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# for crossover: find a random subtree in both trees, and switch them
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# first, select one random mix node from both
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a_nodes = tree_a.root().traverse()
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b_nodes = tree_b.root().traverse()
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a_mix_nodes = []
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b_mix_nodes = []
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for n in a_nodes:
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if type(n) == MixNode:
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a_mix_nodes.append(n)
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for n in b_nodes:
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if type(n) == MixNode:
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b_mix_nodes.append(n)
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a_mix_node = np.random.choice(a_mix_nodes)
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b_mix_node = np.random.choice(b_mix_nodes)
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# now select one random child, we will switch the subtrees there
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a_child = np.random.choice(list(a_mix_node.childs()))
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b_child = np.random.choice(list(b_mix_node.childs()))
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# ...and perform the switch
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# manually remove references
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a_mix_node.remove_child(a_child)
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b_mix_node.remove_child(b_child)
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# and add child to other subtree
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a_mix_node.add_child(b_child)
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b_mix_node.add_child(a_child)
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def hold_best(self, n=10):
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scores = [tree.score() for tree in self.population]
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scores = np.array([tree.score() for tree in self.population])
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sorted_indices = np.argsort(-scores)
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@ -1505,8 +1575,8 @@ class Population(object):
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avg_scores = []
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for i in tqdm(range(n), desc="run evolutionary cycles"):
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self.mutate()
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#self.mutate()
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#self.collect_scores()
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self.crossover()
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self.pairwise_competition()
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#self.collect_scores()
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@ -1530,17 +1600,13 @@ class Population(object):
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# ## Run Evolutionary Algorithm
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#p = Population(["noodle"],['noodle'], min_additional=4, max_additional=13, n_population = 50)
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p = Population(["noodle"],['noodle'], min_additional=4, max_additional=13, n_population = 50, mutations=1)
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#p_ingredient_unprepared(list(p.population[0].root().childs())[0]._name) < 0.2
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#p.run(50)
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#avg = p.run(10)
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#p.plot_population(n_best=20)
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