def branching(tree, left, node_id, criterion, parent):
value = tree.value[node_id]
if tree.n_outputs == 1:
value = value[0, :]
if tree.children_left[
node_id] == _tree.TREE_LEAF and tree.children_right[
node_id] == _tree.TREE_LEAF:
#temp = np.where(value!=0)[0].tolist()
temp = np.where(value == max(value))[0].tolist()
temp = ['__' + str(i + 1) for i in temp]
more = '---> more' if len(temp) > 1 else ''
samples = tree.n_node_samples[node_id]
if delete_more:
if len(temp) > 1:
if left:
if tree.children_right[parent] != _tree.TREE_LEAF:
tree.children_left[parent] = _tree.TREE_LEAF
else:
r = tree.value[
tree.children_right[parent]][0].tolist()
l = value.tolist()
tree.value[node_id][0] = np.array(
[i + j for i, j in zip(l, r)])
else:
if tree.children_left[parent] != _tree.TREE_LEAF:
tree.children_right[parent] = _tree.TREE_LEAF
else:
l = tree.value[
tree.children_left[parent]][0].tolist()
r = value.tolist()
tree.value[node_id][0] = np.array(
[i + j for i, j in zip(l, r)])
return "%s # samples = %s # %s" \
% (temp,
samples,
more)
else:
if feature_names is not None:
feature = feature_names[tree.feature[node_id]]
else:
feature = "X[%s]" % tree.feature[node_id]
#add condition at node to current branch
return "%s <= %.4f samples = %s" \
% (feature,
tree.threshold[node_id],
tree.n_node_samples[node_id])
def branching(tree, left, node_id, criterion, parent):
value = tree.value[node_id]
if tree.n_outputs == 1:
value = value[0, :]
if tree.children_left[node_id] == _tree.TREE_LEAF and tree.children_right[node_id] == _tree.TREE_LEAF:
#temp = np.where(value!=0)[0].tolist()
temp = np.where(value==max(value))[0].tolist()
temp = ['__'+str(i+1) for i in temp]
more = '---> more' if len(temp)>1 else ''
samples = tree.n_node_samples[node_id]
if delete_more:
if len(temp) > 1:
if left:
if tree.children_right[parent] != _tree.TREE_LEAF:
tree.children_left[parent] = _tree.TREE_LEAF
else:
r = tree.value[tree.children_right[parent]][0].tolist()
l = value.tolist()
tree.value[node_id][0] = np.array([i+j for i,j in zip(l,r)])
else:
if tree.children_left[parent] != _tree.TREE_LEAF:
tree.children_right[parent] = _tree.TREE_LEAF
else:
l = tree.value[tree.children_left[parent]][0].tolist()
r = value.tolist()
tree.value[node_id][0] = np.array([i+j for i,j in zip(l,r)])
return "%s # samples = %s # %s" \
% (temp,
samples,
more)
else:
if feature_names is not None:
feature = feature_names[tree.feature[node_id]]
else:
feature = "X[%s]" % tree.feature[node_id]
#add condition at node to current branch
return "%s <= %.4f samples = %s" \
% (feature,
tree.threshold[node_id],
tree.n_node_samples[node_id])
def collect(clus,t,l,n_id,c,p):
val = t.value[n_id]
if t.n_outputs == 1:
val = val[0,:]
test_for_exclusives = np.equal(val,np.array([0.0]*len(val)))
if False in test_for_exclusives:
clu = np.where(val==max(val))[0].tolist()
for i in clu:
if i not in clus: clus.append(i)
return clus
def collect(clus, t, l, n_id, c, p):
val = t.value[n_id]
if t.n_outputs == 1:
val = val[0, :]
test_for_exclusives = np.equal(val, np.array([0.0] * len(val)))
if False in test_for_exclusives:
clu = np.where(val == max(val))[0].tolist()
for i in clu:
if i not in clus: clus.append(i)
return clus