首次提交本地代码
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414
method/MtadGat.py
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414
method/MtadGat.py
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"""
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github找的第三方实现的Mtad-Gat源码
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由于源码使用了torch.empty，经常导致loss为nan，因此替换为了torch.zeros
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.data as tud
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class ConvLayer(nn.Module):
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"""1-D Convolution layer to extract high-level features of each time-series input
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:param n_features: Number of input features/nodes
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:param window_size: length of the input sequence
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:param kernel_size: size of kernel to use in the convolution operation
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"""
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def __init__(self, n_features, kernel_size=7):
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super(ConvLayer, self).__init__()
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self.padding = nn.ConstantPad1d((kernel_size - 1) // 2, 0.0)
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self.conv = nn.Conv1d(in_channels=n_features, out_channels=n_features, kernel_size=kernel_size)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = x.permute(0, 2, 1)
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x = self.padding(x)
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x = self.relu(self.conv(x))
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return x.permute(0, 2, 1) # Permute back
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class FeatureAttentionLayer(nn.Module):
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"""Single Graph Feature/Spatial Attention Layer
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:param n_features: Number of input features/nodes
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:param window_size: length of the input sequence
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:param dropout: percentage of nodes to dropout
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:param alpha: negative slope used in the leaky rely activation function
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:param embed_dim: embedding dimension (output dimension of linear transformation)
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:param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT
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:param use_bias: whether to include a bias term in the attention layer
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"""
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def __init__(self, n_features, window_size, dropout, alpha, embed_dim=None, use_gatv2=True, use_bias=True):
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super(FeatureAttentionLayer, self).__init__()
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self.n_features = n_features
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self.window_size = window_size
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self.dropout = dropout
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self.embed_dim = embed_dim if embed_dim is not None else window_size
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self.use_gatv2 = use_gatv2
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self.num_nodes = n_features
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self.use_bias = use_bias
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# Because linear transformation is done after concatenation in GATv2
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if self.use_gatv2:
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self.embed_dim *= 2
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lin_input_dim = 2 * window_size
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a_input_dim = self.embed_dim
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else:
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lin_input_dim = window_size
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a_input_dim = 2 * self.embed_dim
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self.lin = nn.Linear(lin_input_dim, self.embed_dim)
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# self.a = nn.Parameter(torch.empty((a_input_dim, 1)))
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self.a = nn.Parameter(torch.zeros((a_input_dim, 1)))
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nn.init.xavier_uniform_(self.a.data, gain=1.414)
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if self.use_bias:
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# self.bias = nn.Parameter(torch.empty(n_features, n_features))
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self.bias = nn.Parameter(torch.zeros(n_features, n_features))
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self.leakyrelu = nn.LeakyReLU(alpha)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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# x shape (b, n, k): b - batch size, n - window size, k - number of features
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# For feature attention we represent a node as the values of a particular feature across all timestamps
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x = x.permute(0, 2, 1)
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# 'Dynamic' GAT attention
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# Proposed by Brody et. al., 2021 (https://arxiv.org/pdf/2105.14491.pdf)
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# Linear transformation applied after concatenation and attention layer applied after leakyrelu
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if self.use_gatv2:
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a_input = self._make_attention_input(x) # (b, k, k, 2*window_size)
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a_input = self.leakyrelu(self.lin(a_input)) # (b, k, k, embed_dim)
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e = torch.matmul(a_input, self.a).squeeze(3) # (b, k, k, 1)
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# Original GAT attention
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else:
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Wx = self.lin(x) # (b, k, k, embed_dim)
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a_input = self._make_attention_input(Wx) # (b, k, k, 2*embed_dim)
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e = self.leakyrelu(torch.matmul(a_input, self.a)).squeeze(3) # (b, k, k, 1)
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if self.use_bias:
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e += self.bias
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# Attention weights
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attention = torch.softmax(e, dim=2)
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attention = torch.dropout(attention, self.dropout, train=self.training)
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# Computing new node features using the attention
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h = self.sigmoid(torch.matmul(attention, x))
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return h.permute(0, 2, 1)
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def _make_attention_input(self, v):
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"""Preparing the feature attention mechanism.
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Creating matrix with all possible combinations of concatenations of node.
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Each node consists of all values of that node within the window
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v1 || v1,
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...
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v1 || vK,
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v2 || v1,
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...
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v2 || vK,
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...
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...
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vK || v1,
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...
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vK || vK,
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"""
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K = self.num_nodes
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blocks_repeating = v.repeat_interleave(K, dim=1) # Left-side of the matrix
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blocks_alternating = v.repeat(1, K, 1) # Right-side of the matrix
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combined = torch.cat((blocks_repeating, blocks_alternating), dim=2) # (b, K*K, 2*window_size)
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if self.use_gatv2:
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return combined.view(v.size(0), K, K, 2 * self.window_size)
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else:
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return combined.view(v.size(0), K, K, 2 * self.embed_dim)
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class TemporalAttentionLayer(nn.Module):
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"""Single Graph Temporal Attention Layer
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:param n_features: number of input features/nodes
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:param window_size: length of the input sequence
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:param dropout: percentage of nodes to dropout
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:param alpha: negative slope used in the leaky rely activation function
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:param embed_dim: embedding dimension (output dimension of linear transformation)
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:param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT
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:param use_bias: whether to include a bias term in the attention layer
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"""
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def __init__(self, n_features, window_size, dropout, alpha, embed_dim=None, use_gatv2=True, use_bias=True):
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super(TemporalAttentionLayer, self).__init__()
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self.n_features = n_features
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self.window_size = window_size
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self.dropout = dropout
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self.use_gatv2 = use_gatv2
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self.embed_dim = embed_dim if embed_dim is not None else n_features
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self.num_nodes = window_size
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self.use_bias = use_bias
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# Because linear transformation is performed after concatenation in GATv2
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if self.use_gatv2:
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self.embed_dim *= 2
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lin_input_dim = 2 * n_features
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a_input_dim = self.embed_dim
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else:
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lin_input_dim = n_features
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a_input_dim = 2 * self.embed_dim
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self.lin = nn.Linear(lin_input_dim, self.embed_dim)
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# self.a = nn.Parameter(torch.empty((a_input_dim, 1)))
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self.a = nn.Parameter(torch.zeros((a_input_dim, 1)))
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nn.init.xavier_uniform_(self.a.data, gain=1.414)
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if self.use_bias:
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# self.bias = nn.Parameter(torch.empty(window_size, window_size))
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self.bias = nn.Parameter(torch.zeros(window_size, window_size))
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self.leakyrelu = nn.LeakyReLU(alpha)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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# x shape (b, n, k): b - batch size, n - window size, k - number of features
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# For temporal attention a node is represented as all feature values at a specific timestamp
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# 'Dynamic' GAT attention
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# Proposed by Brody et. al., 2021 (https://arxiv.org/pdf/2105.14491.pdf)
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# Linear transformation applied after concatenation and attention layer applied after leakyrelu
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if self.use_gatv2:
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a_input = self._make_attention_input(x) # (b, n, n, 2*n_features)
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a_input = self.leakyrelu(self.lin(a_input)) # (b, n, n, embed_dim)
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e = torch.matmul(a_input, self.a).squeeze(3) # (b, n, n, 1)
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# Original GAT attention
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else:
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Wx = self.lin(x) # (b, n, n, embed_dim)
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a_input = self._make_attention_input(Wx) # (b, n, n, 2*embed_dim)
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e = self.leakyrelu(torch.matmul(a_input, self.a)).squeeze(3) # (b, n, n, 1)
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if self.use_bias:
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e += self.bias # (b, n, n, 1)
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# Attention weights
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attention = torch.softmax(e, dim=2)
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attention = torch.dropout(attention, self.dropout, train=self.training)
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h = self.sigmoid(torch.matmul(attention, x)) # (b, n, k)
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return h
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def _make_attention_input(self, v):
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"""Preparing the temporal attention mechanism.
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Creating matrix with all possible combinations of concatenations of node values:
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(v1, v2..)_t1 || (v1, v2..)_t1
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(v1, v2..)_t1 || (v1, v2..)_t2
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...
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...
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(v1, v2..)_tn || (v1, v2..)_t1
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(v1, v2..)_tn || (v1, v2..)_t2
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"""
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K = self.num_nodes
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blocks_repeating = v.repeat_interleave(K, dim=1) # Left-side of the matrix
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blocks_alternating = v.repeat(1, K, 1) # Right-side of the matrix
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combined = torch.cat((blocks_repeating, blocks_alternating), dim=2)
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if self.use_gatv2:
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return combined.view(v.size(0), K, K, 2 * self.n_features)
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else:
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return combined.view(v.size(0), K, K, 2 * self.embed_dim)
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class GRULayer(nn.Module):
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"""Gated Recurrent Unit (GRU) Layer
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:param in_dim: number of input features
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:param hid_dim: hidden size of the GRU
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:param n_layers: number of layers in GRU
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:param dropout: dropout rate
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"""
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def __init__(self, in_dim, hid_dim, n_layers, dropout):
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super(GRULayer, self).__init__()
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self.hid_dim = hid_dim
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self.n_layers = n_layers
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self.dropout = 0.0 if n_layers == 1 else dropout
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self.gru = nn.GRU(in_dim, hid_dim, num_layers=n_layers, batch_first=True, dropout=self.dropout)
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def forward(self, x):
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out, h = self.gru(x)
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out, h = out[-1, :, :], h[-1, :, :] # Extracting from last layer
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return out, h
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class RNNDecoder(nn.Module):
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"""GRU-based Decoder network that converts latent vector into output
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:param in_dim: number of input features
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:param n_layers: number of layers in RNN
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:param hid_dim: hidden size of the RNN
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:param dropout: dropout rate
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"""
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def __init__(self, in_dim, hid_dim, n_layers, dropout):
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super(RNNDecoder, self).__init__()
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self.in_dim = in_dim
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self.dropout = 0.0 if n_layers == 1 else dropout
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self.rnn = nn.GRU(in_dim, hid_dim, n_layers, batch_first=True, dropout=self.dropout)
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def forward(self, x):
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decoder_out, _ = self.rnn(x)
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return decoder_out
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class ReconstructionModel(nn.Module):
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"""Reconstruction Model
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:param window_size: length of the input sequence
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:param in_dim: number of input features
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:param n_layers: number of layers in RNN
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:param hid_dim: hidden size of the RNN
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:param in_dim: number of output features
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:param dropout: dropout rate
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"""
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def __init__(self, window_size, in_dim, hid_dim, out_dim, n_layers, dropout):
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super(ReconstructionModel, self).__init__()
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self.window_size = window_size
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self.decoder = RNNDecoder(in_dim, hid_dim, n_layers, dropout)
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self.fc = nn.Linear(hid_dim, out_dim)
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def forward(self, x):
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# x will be last hidden state of the GRU layer
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h_end = x
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h_end_rep = h_end.repeat_interleave(self.window_size, dim=1).view(x.size(0), self.window_size, -1)
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decoder_out = self.decoder(h_end_rep)
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out = self.fc(decoder_out)
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return out
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class Forecasting_Model(nn.Module):
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"""Forecasting model (fully-connected network)
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:param in_dim: number of input features
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:param hid_dim: hidden size of the FC network
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:param out_dim: number of output features
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:param n_layers: number of FC layers
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:param dropout: dropout rate
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"""
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def __init__(self, in_dim, hid_dim, out_dim, n_layers, dropout):
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super(Forecasting_Model, self).__init__()
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layers = [nn.Linear(in_dim, hid_dim)]
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for _ in range(n_layers - 1):
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layers.append(nn.Linear(hid_dim, hid_dim))
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layers.append(nn.Linear(hid_dim, out_dim))
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self.layers = nn.ModuleList(layers)
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self.dropout = nn.Dropout(dropout)
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self.relu = nn.ReLU()
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def forward(self, x):
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for i in range(len(self.layers) - 1):
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x = self.relu(self.layers[i](x))
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x = self.dropout(x)
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return self.layers[-1](x)
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class Model(nn.Module):
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""" MTAD_GAT model class.
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:param n_features: Number of input features
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:param window_size: Length of the input sequence
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:param out_dim: Number of features to output
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:param kernel_size: size of kernel to use in the 1-D convolution
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:param feat_gat_embed_dim: embedding dimension (output dimension of linear transformation)
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in feat-oriented GAT layer
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:param time_gat_embed_dim: embedding dimension (output dimension of linear transformation)
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in time-oriented GAT layer
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:param use_gatv2: whether to use the modified attention mechanism of GATv2 instead of standard GAT
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:param gru_n_layers: number of layers in the GRU layer
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:param gru_hid_dim: hidden dimension in the GRU layer
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:param forecast_n_layers: number of layers in the FC-based Forecasting Model
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:param forecast_hid_dim: hidden dimension in the FC-based Forecasting Model
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:param recon_n_layers: number of layers in the GRU-based Reconstruction Model
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:param recon_hid_dim: hidden dimension in the GRU-based Reconstruction Model
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:param dropout: dropout rate
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:param alpha: negative slope used in the leaky rely activation function
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"""
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def __init__(self, customs: {}, dataloader: tud.DataLoader):
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super(Model, self).__init__()
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n_features = dataloader.dataset.train_inputs.shape[-1]
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window_size = int(customs["input_size"])
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out_dim = n_features
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kernel_size = 7
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feat_gat_embed_dim = None
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time_gat_embed_dim = None
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use_gatv2 = True
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gru_n_layers = 1
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gru_hid_dim = 150
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forecast_n_layers = 1
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forecast_hid_dim = 150
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recon_n_layers = 1
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recon_hid_dim = 150
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dropout = 0.2
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alpha = 0.2
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self.name = "MtadGat"
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self.conv = ConvLayer(n_features, kernel_size)
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self.feature_gat = FeatureAttentionLayer(
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n_features, window_size, dropout, alpha, feat_gat_embed_dim, use_gatv2)
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self.temporal_gat = TemporalAttentionLayer(n_features, window_size, dropout, alpha, time_gat_embed_dim,
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use_gatv2)
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self.gru = GRULayer(3 * n_features, gru_hid_dim, gru_n_layers, dropout)
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self.forecasting_model = Forecasting_Model(
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gru_hid_dim, forecast_hid_dim, out_dim, forecast_n_layers, dropout)
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self.recon_model = ReconstructionModel(window_size, gru_hid_dim, recon_hid_dim, out_dim, recon_n_layers,
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dropout)
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def forward(self, x):
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# x shape (b, n, k): b - batch size, n - window size, k - number of features
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x = self.conv(x)
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h_feat = self.feature_gat(x)
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h_temp = self.temporal_gat(x)
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h_cat = torch.cat([x, h_feat, h_temp], dim=2) # (b, n, 3k)
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_, h_end = self.gru(h_cat)
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h_end = h_end.view(x.shape[0], -1) # Hidden state for last timestamp
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predictions = self.forecasting_model(h_end)
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recons = self.recon_model(h_end)
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return predictions, recons
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def loss(self, x, y_true, epoch: int = None, device: str = "cpu"):
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preds, recons = self.forward(x)
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if preds.ndim == 3:
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preds = preds.squeeze(1)
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if y_true.ndim == 3:
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y_true = y_true.squeeze(1)
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forecast_criterion = nn.MSELoss()
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recon_criterion = nn.MSELoss()
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forecast_loss = torch.sqrt(forecast_criterion(y_true, preds))
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recon_loss = torch.sqrt(recon_criterion(x, recons))
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loss = forecast_loss + recon_loss
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loss.backward()
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return loss.item()
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def detection(self, x, y_true, epoch: int = None, device: str = "cpu"):
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preds, recons = self.forward(x)
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score = F.pairwise_distance(recons.reshape(recons.size(0), -1), x.reshape(x.size(0), -1)) + \
|
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F.pairwise_distance(y_true.reshape(y_true.size(0), -1), preds.reshape(preds.size(0), -1))
|
||||
return score, None
|
||||
|
||||
|
||||
Reference in New Issue
Block a user