def crossover(tree, trees, CR, X):
partners = random.sample(trees, 3)
partners = [ClassificationTree.copy_tree(partners[0]), ClassificationTree.copy_tree(partners[1]), ClassificationTree.copy_tree(partners[2])]
tree_nodes = list(tree.tree.values())
d = random.choice(range(1, depth))
#for d in reversed(range(tree.depth-1)):
nodes_at_depth = ClassificationTree.get_nodes_at_depth(d, tree)
other_nodes_at_depth = ClassificationTree.get_nodes_at_depth(d, partners[0])
#other_nodes_at_depth.extend(ClassificationTree.get_nodes_at_depth(d, partners[1]))
#other_nodes_at_depth.extend(ClassificationTree.get_nodes_at_depth(d, partners[2]))
#for node in nodes_at_depth:
node = random.choice(nodes_at_depth)
p = np.random.uniform()
if p < CR:
choice = random.choice(other_nodes_at_depth)
if choice.left_node != None and not choice.left_node.is_leaf and choice.right_node != None and not choice.right_node.is_leaf:
p2 = np.random.uniform()
if p2 < 0.5:
node.feature = choice.left_node.feature
node.threshold = choice.left_node.threshold
#ClassificationTree.replace_node(node, choice.left_node, tree)
else:
node.feature = choice.right_node.feature
node.threshold = choice.right_node.threshold
#ClassificationTree.replace_node(node, choice.right_node, tree)
else:
#ClassificationTree.replace_node(node, choice, tree)
node.feature = choice.feature
node.threshold = choice.threshold
'''
if choice.parent_id != -1:
node.feature = tree.tree[choice.parent_id].feature
node.threshold = tree.tree[choice.parent_id].threshold
'''
else:
node.feature = random.choice(range(len(X[0])))
node.threshold = 0
def optimize_evolution(tree, trees, X, labels, X_valid, y_valid, CR):
trial = ClassificationTree.copy_tree(tree)
'''
n_nodes = len(X[0])/(2**tree.depth - 1)
if n_nodes < 1:
n_nodes = 1
p = np.random.uniform()
for node in nodes:
if p < CR and not node.is_leaf:
node.feature = np.random.randint(0, len(X[0]))
node.threshold = np.random.uniform(np.min(X[node.feature]),np.max(X[node.feature]))
'''
crossover(trial, trees, CR, X)
ClassificationTree.build_idxs_of_subtree(X, range(len(labels)), trial.tree[0], oblique)
#partners = random.sample(trees, 2)
#optimize_crossover(trial, ClassificationTree.copy_tree(partners[0]), ClassificationTree.copy_tree(partners[1]), depth, X, labels, oblique)
tao_opt(trial, X, labels)
loss = regularized_loss(trial.tree[0], X, labels, X_valid, y_valid, range(len(labels)), oblique)
#Se il nuovo individuo è migliore del padre li scambio
if loss < regularized_loss(tree.tree[0], X, labels, X_valid, y_valid, range(len(labels)), oblique):
#if loss < ClassificationTree.misclassification_loss(tree.tree[0], X, labels, range(len(labels)), oblique):
tree = trial