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forest.py
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forest.py
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import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import numpy as np
import pickle
from utils.gaussian import gaussian_func
class Tree(nn.Module):
def __init__(self,depth,n_in_feature):
super(Tree, self).__init__()
self.depth = depth
self.n_leaf = 2 ** (depth - 1)
# used features in this tree
n_used_feature = self.n_leaf - 1
onehot = np.eye(n_in_feature)
using_idx = np.random.choice(np.arange(n_in_feature), n_used_feature, replace=False)
self.feature_mask = onehot[using_idx].T
self.feature_mask = Parameter(torch.from_numpy(self.feature_mask).type(torch.FloatTensor),requires_grad=False)
def forward(self,x):
"""
:param x(Variable): [batch_size,n_features]
:return: route probability (Variable): [batch_size,n_leaf]
"""
if x.is_cuda and not self.feature_mask.is_cuda:
self.feature_mask = self.feature_mask.cuda()
feats = torch.mm(x,self.feature_mask) # ->[batch_size,n_used_feature]
decision = torch.sigmoid(feats) # ->[batch_size,n_leaf - 1]
decision = torch.unsqueeze(decision,dim=2)
decision_comp = 1-decision
decision = torch.cat((decision,decision_comp),dim=2) # -> [batch_size,n_leaf,2]
# compute route probability
batch_size = x.size()[0]
_mu = Variable(x.data.new(batch_size,1,1).fill_(1.))
begin_idx = 0
end_idx = 1
for n_layer in range(0, self.depth - 1):
_mu = _mu.view(batch_size,-1,1).repeat(1,1,2)
_decision = decision[:, begin_idx:end_idx, :] # -> [batch_size,2**n_layer,2]
_mu = _mu*_decision # -> [batch_size,2**n_layer,2]
begin_idx = end_idx
end_idx = begin_idx + 2 ** (n_layer+1)
mu = _mu.view(batch_size,self.n_leaf)
return mu
class Forest(nn.Module):
'''
:param \n
n_tree, tree_depth, n_in_feature, num_classes, iterations_update_forest
'''
def __init__(self,n_tree,tree_depth,n_in_feature, num_classes, iterations_update_forest):
super(Forest, self).__init__()
self.trees = nn.ModuleList()
self.n_tree = n_tree
self.num_classes = num_classes
self.dist = Pi(n_tree, tree_depth, iter_num=iterations_update_forest)
for _ in range(n_tree):
tree = Tree(tree_depth,n_in_feature)
self.trees.append(tree)
def forward(self,x):
probs = []
for tree in self.trees:
mu = tree(x)
probs.append(mu.unsqueeze(2))
pi = self.dist.get_mean() # 5, 32
probs = torch.cat(probs,dim=2) # bs, 32, 5
prob = probs * pi.transpose(0, 1).unsqueeze(0)
prob = torch.sum(prob, dim=1)
return prob, probs
class NeuralDecisionForest(nn.Module):
def __init__(self, feature_layer, forest):
super(NeuralDecisionForest, self).__init__()
self.feature_layer = feature_layer
self.forest = forest
def forward(self, x):
out = self.feature_layer(x)
out = out.view(x.size()[0],-1)
out = self.forest(out)
return out
class Pi():
def __init__(self, num_tree, tree_depth, iter_num=20, task_num=1):
leaf_node_per_tree = 2 ** (tree_depth - 1)
self.mean = np.random.rand(num_tree, leaf_node_per_tree, task_num, 1).astype(np.float32)
self.sigma = np.random.rand(num_tree, leaf_node_per_tree, task_num, task_num).astype(np.float32)
self.iter_num = iter_num
def init_kmeans(self, mean, sigma):
print('initialize mean by k-means')
_, leaf_n, _, _ = self.mean.shape
for i in range(leaf_n):
self.mean[:, i, :, :] = mean[i]
self.sigma[:, i, :, :] = sigma[i]
def get_mean(self, cuda=True):
if cuda:
return torch.tensor(self.mean).squeeze().cuda()
else:
return torch.tensor(self.mean).squeeze()
def update(self, x, y):
"""
x has the shape of [samples, num_tree, leaf_num],
y hsa the shape of [samples, 1]
gaussian_function will return a probability \\
array with shape of [samples, num_tree, leaf_num].
"""
print('update PI')
num_tree, leaf_num, _, _ = self.mean.shape
for i in range(self.iter_num):
gaussian_value = gaussian_func(y, self.mean, self.sigma) # [samples, num_tree, leaf_num]
all_leaf_prob_pi = x * (gaussian_value + 1e-9) # [samples, num_tree, leaf_num]
all_leaf_sum_prob = np.sum(all_leaf_prob_pi, axis=2, keepdims=True) #[samples, num_tree, 1]
zeta = all_leaf_prob_pi / (all_leaf_sum_prob + 1e-9) # [samples, num_tree, leaf_num]
y_temp = np.expand_dims(y, 2)
y_temp = np.repeat(y_temp, num_tree, 1)
y_temp = np.repeat(y_temp, leaf_num, 2)
zeta_y = zeta * y_temp # [samples, num_tree, leaf_num]
zeta_y = np.sum(zeta_y, 0) # [num_tree, leaf_num]
zeta_sum = np.sum(zeta, 0) # [num_tree, leaf_num]
mean = zeta_y / (zeta_sum + 1e-9)
self.mean[:,:, 0, 0] = mean
mean_new = y_temp - np.expand_dims(mean, 0) # [samples, num_tree, leaf_num]
zeta_for_sigma = zeta * mean_new * mean_new
zeta_for_sigma = np.sum(zeta_for_sigma, 0)
sigma = zeta_for_sigma / (zeta_sum + 1e-9)
self.sigma[:,:,0,0] = sigma
def save_model(self, path, pace, epoch):
print('save PI at %s' % (path + str(pace) + 'pi_' + str(epoch)))
with open(path + str(pace) + 'pi_' + str(epoch),'wb') as f:
pickle.dump(self.mean, f)
pickle.dump(self.sigma, f)
def load_model(self, path, pace, epoch):
print('load PI from %s' % (path + str(pace) + 'pi_' + str(epoch)))
with open(path + str(pace) + 'pi_' + str(epoch) ,'rb') as f:
self.mean = pickle.load(f)
self.sigma = pickle.load(f)
print('load PI successfully!')