def best_svm_split(self, node, X, y, alfa, complexity):
T = self.classification_tree
#Creo un nuovo sottoalbero fittizio con root nel nodo
new_node = TreeNode.copy_node(node)
#if new_node.is_leaf:
#complexity += 1
rho = 0
data = X[new_node.data_idxs]
label = y[new_node.data_idxs]
if not all(i == label[0] for i in label) and len(data) > 0:
#Questo fa SVM multiclasse 1 vs rest
clf = LinearSVC(tol=1e-6,
C=10,
max_iter=10000,
loss='squared_hinge',
penalty='l1',
dual=False)
clf.fit(data, label)
#for n_class in range(n_classes):
#n_misclassified = np.count_nonzero(label-np.sign(np.dot(data, clf.coef_[n_class].T)+clf.intercept_[n_class]))
#Devo capire come ottenere i coefficienti migliori tra il numero di iperpiani addestrati
weights = clf.coef_.reshape((len(X[0])), )
intercept = clf.intercept_
if new_node.is_leaf:
ClassificationTree.create_new_children(node,
X,
y,
self.max_id,
None,
None,
oblique=T.oblique,
weights=weights,
intercept=intercept)
rho = 1
else:
new_node.weights = weights
new_node.intercept = intercept
return new_node, ClassificationTree.misclassification_loss(
new_node, X, y, new_node.data_idxs,
T.oblique) + alfa * (complexity + rho)
return new_node, np.inf
def best_parallel_split(self, node, X, y, alfa, complexity):
T = self.classification_tree
#Creo un nuovo sottoalbero fittizio con root nel nodo
new_node = TreeNode.copy_node(node)
error_best = np.inf
#if new_node.is_leaf:
#complexity += 1
was_leaf = False
improve = False
rho = 0
if new_node.is_leaf:
was_leaf = True
rho = 1
if new_node.data_idxs:
for j in range(len(X[0])):
#Prendo tutte le j-esime componenti del dataset e le ordino
#in modo crescente
vals = {}
for point_idx in new_node.data_idxs:
vals[point_idx] = X[point_idx, j]
values = sorted(vals.items(), key=lambda x: x[1])
sorted_indexes = [tuple[0] for tuple in values]
#plt.scatter(X[sorted_indexes, j], range(len(values)), s=0.4, c=list(correct_classification_tuples.values()))
#plt.show()
new_node.feature = j
#if j==2:
#base = actual_loss
#actual_loss = self.binary_loss(node_id, X, y, sorted_indexes[i], correct_classification_tuples[sorted_indexes[i]], actual_loss, thresh)
#print ("loss: ", actual_loss, "n punti: ", len(care_points_idxs))
#print("vecchia: ", self.vecchia_loss(node_id, X, y, care_points_idxs, correct_classification_tuples, thresh))
#Ciclo su ogni valore di quella componente e provo tutti gli split
#possibili
'''
new_node.threshold = 0.5*X[sorted_indexes[0], j]
'''
i = -1
actual_loss = ClassificationTree.misclassification_loss(
new_node, X, y, sorted_indexes, self.classification_tree.
oblique) + alfa * (complexity + rho)
while i < len(sorted_indexes):
pre_thresh = new_node.threshold
#print("Ottimizzo best parallel: ", i*100/len(sorted_indexes))
if i < 0:
thresh = 0.5 * X[sorted_indexes[0], j]
if i < len(sorted_indexes) - 1:
thresh = 0.5 * (X[sorted_indexes[i], j] +
X[sorted_indexes[i + 1], j])
else:
thresh = 1.5 * X[sorted_indexes[i], j]
new_node.threshold = thresh
'''
#Se il nodo da ottimizzare era una foglia allora dobbiamo creare le foglie figlie
#queste vengono ottimizzate subito per maggioranza
if was_leaf:
self.create_new_children(new_node, j, thresh)
inc, k = self.binary_loss(X, new_node, sorted_indexes, i, y, pre_thresh)
actual_loss += inc
else:
inc, k = self.binary_loss(X, new_node, sorted_indexes, i, y, pre_thresh)
actual_loss += inc
'''
#Se il nodo da ottimizzare era una foglia allora dobbiamo creare le foglie figlie
#queste vengono ottimizzate subito per maggioranza
if was_leaf:
ClassificationTree.create_new_children(
new_node,
X,
y,
self.max_id,
j,
thresh,
oblique=T.oblique)
actual_loss = ClassificationTree.misclassification_loss(
new_node, X, y, sorted_indexes,
self.classification_tree.oblique) + alfa * (
complexity + rho)
if actual_loss < error_best:
improve = True
error_best = actual_loss
best_t = thresh
best_feature = j
i += 1
#print ("error best: ", error_best)
new_node.threshold = best_t
new_node.feature = best_feature
if was_leaf and improve:
self.max_id += 2
return new_node, error_best
