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Fix model bug and push (#35)

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you-n-g
2024-06-27 18:15:31 +08:00
committed by GitHub
parent f25ef70c98
commit bc8d96e96c
3 changed files with 38 additions and 10 deletions
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rdagent/model_implementation/task.py
+25 -8
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@@ -41,18 +41,35 @@ class ModelTaskLoderJson(TaskLoader):
def load(self, *argT, **kwargs) -> Sequence[ModelImplTask]:
# TODO: we should load the tasks from json;
# this version does not align with the right answer
# formula_info = {
# "name": "Anti-Symmetric Deep Graph Network (A-DGN)",
# "description": "A framework for stable and non-dissipative DGN design. It ensures long-range information preservation between nodes and prevents gradient vanishing or explosion during training.",
# "formulation": "x_u^{(l)} = x_u^{(l-1)} + \\epsilon \\sigma \\left( W^T x_u^{(l-1)} + \\Phi(X^{(l-1)}, N_u) + b \\right)",
# "variables": {
# "x_u^{(l)}": "The state of node u at layer l",
# "\\epsilon": "The step size in the Euler discretization",
# "\\sigma": "A monotonically non-decreasing activation function",
# "W": "An anti-symmetric weight matrix",
# "X^{(l-1)}": "The node feature matrix at layer l-1",
# "N_u": "The set of neighbors of node u",
# "b": "A bias vector",
# },
# "key": "A-DGN",
# }
formula_info = {
"name": "Anti-Symmetric Deep Graph Network (A-DGN)",
"description": "A framework for stable and non-dissipative DGN design. It ensures long-range information preservation between nodes and prevents gradient vanishing or explosion during training.",
"formulation": "x_u^{(l)} = x_u^{(l-1)} + \\epsilon \\sigma \\left( W^T x_u^{(l-1)} + \\Phi(X^{(l-1)}, N_u) + b \\right)",
"formulation": r"\mathbf{x}^{\prime}_i = \mathbf{x}_i + \epsilon \cdot \sigma \left( (\mathbf{W}-\mathbf{W}^T-\gamma \mathbf{I}) \mathbf{x}_i + \Phi(\mathbf{X}, \mathcal{N}_i) + \mathbf{b}\right),",
"variables": {
"x_u^{(l)}": "The state of node u at layer l",
"\\epsilon": "The step size in the Euler discretization",
"\\sigma": "A monotonically non-decreasing activation function",
"W": "An anti-symmetric weight matrix",
"X^{(l-1)}": "The node feature matrix at layer l-1",
"N_u": "The set of neighbors of node u",
"b": "A bias vector",
r"\mathbf{x}_i": "The state of node i at previous layer",
r"\epsilon": "The step size in the Euler discretization",
r"\sigma": "A monotonically non-decreasing activation function",
r"\Phi": "A graph convolutional operator",
r"W": "An anti-symmetric weight matrix",
r"\mathbf{x}^{\prime}_i": "The node feature matrix at layer l-1",
r"\mathcal{N}_i": "The set of neighbors of node u",
r"\mathbf{b}": "A bias vector",
},
"key": "A-DGN",
}