关于split_gain的问题

[Aliang-CN]

您好：
我在阅读您的代码的时候发现一个问题，self.gain = getItemByTree(self, 'split_gain')，这行代码应该是获取节点每次分裂的信息增益，但是在getItemByTree里面的getFeature里面并没有相对应的操作。
def getItemByTree(tree, item='split_feature'):
root = tree.raw['tree_structure']
split_nodes = tree.split_nodes
res = np.zeros(split_nodes+tree.raw['num_leaves'], dtype=np.int32)
if 'value' in item or 'threshold' in item or 'split_gain' in item:
res = res.astype(np.float64)
def getFeature(root, res):
if 'child' in item:
if 'split_index' in root:
node = root[item]
if 'split_index' in node:
res[root['split_index']] = node['split_index']
else:
res[root['split_index']] = node['leaf_index'] + split_nodes # need to check
else:
res[root['leaf_index'] + split_nodes] = -1
elif 'value' in item:
if 'split_index' in root:
res[root['split_index']] = root['internal_'+item]
else:
res[root['leaf_index'] + split_nodes] = root['leaf_'+item]
else:
if 'split_index' in root:
res[root['split_index']] = root[item]
else:
res[root['leaf_index'] + split_nodes] = -2
if 'left_child' in root:
getFeature(root['left_child'], res)
if 'right_child' in root:
getFeature(root['right_child'], res)
getFeature(root, res)
return res

[Aliang-CN]

您好，可以更新一下最新的模型吗？

[Aliang-CN]

您好！
还有一个问题想咨询一下。在SubGBDTLeaf_cls函数里面下面这段代码中的all_hav主要是记录什么信息呢？然后下面treeI[tree].gain[kdx]，实际上应该是记录split _feature,我这么理解可以吗？
all_hav = {} # set([i for i in range(MAX)])
for jdx, tree in enumerate(tree_indices):
for kdx, f in enumerate(treeI[tree].feature):
if f == -2:
continue
if f not in all_hav:
all_hav[f] = 0
all_hav[f] += treeI[tree].gain[kdx]

[Aliang-CN]

您好！
还有一个问题想咨询你，vectors[idx] = set(features[np.where(features>0)]) ，这行代码是过滤split_feature>0的特征, 那么split_feature=0的这个特征就会被遗留。
def EqualGroup(self, n_clusters, args):
vectors = {}
# n_feature = 256
for idx,features in enumerate(self.featurelist):
vectors[idx] = set(features[np.where(features>0)])
keys = random.sample(vectors.keys(), len(vectors))
clusterIdx = np.zeros(len(vectors))
groups = [[] for i in range(n_clusters)]
trees_per_cluster = len(vectors)//n_clusters
mod_per_cluster = len(vectors) % n_clusters
begin = 0
for idx in range(n_clusters):
for jdx in range(trees_per_cluster):
clusterIdx[keys[begin]] = idx
begin += 1
if idx < mod_per_cluster:
clusterIdx[keys[begin]] = idx
begin += 1
print([np.where(clusterIdx==i)[0].shape for i in range(n_clusters)])
return clusterIdx

[motefly]

您好：
我在阅读您的代码的时候发现一个问题，self.gain = getItemByTree(self, 'split_gain')，这行代码应该是获取节点每次分裂的信息增益，但是在getItemByTree里面的getFeature里面并没有相对应的操作。
def getItemByTree(tree, item='split_feature'):
root = tree.raw['tree_structure']
split_nodes = tree.split_nodes
res = np.zeros(split_nodes+tree.raw['num_leaves'], dtype=np.int32)
if 'value' in item or 'threshold' in item or 'split_gain' in item:
res = res.astype(np.float64)
def getFeature(root, res):
if 'child' in item:
if 'split_index' in root:
node = root[item]
if 'split_index' in node:
res[root['split_index']] = node['split_index']
else:
res[root['split_index']] = node['leaf_index'] + split_nodes # need to check
else:
res[root['leaf_index'] + split_nodes] = -1
elif 'value' in item:
if 'split_index' in root:
res[root['split_index']] = root['internal_'+item]
else:
res[root['leaf_index'] + split_nodes] = root['leaf_'+item]
else:
if 'split_index' in root:
res[root['split_index']] = root[item]
else:
res[root['leaf_index'] + split_nodes] = -2
if 'left_child' in root:
getFeature(root['left_child'], res)
if 'right_child' in root:
getFeature(root['right_child'], res)
getFeature(root, res)
return res

您好，之前我误判了，https://github.com/motefly/DeepGBM/blob/master/tree_model_interpreter.py#L36 这里应该是可以拿到gain的。