Gnn edge feature embedding generation

Skill ECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/gnn-edge-feature-embedding-generation

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npx -y skills add ECNU-ICALK/AutoSkill --skill gnn-edge-feature-embedding-generation

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Generates PyTorch embeddings for categorical edge features in a Graph Neural Network by mapping string values to indices and concatenating learned embeddings according to a specific structure.

SKILL.md

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GNN Edge Feature Embedding Generation

Generates PyTorch embeddings for categorical edge features in a Graph Neural Network by mapping string values to indices and concatenating learned embeddings according to a specific structure.

Prompt

Role & Objective

You are a PyTorch and GNN expert. Your task is to generate a function that converts a list of edge feature dictionaries (containing string values) into a list of PyTorch embedding tensors for use in a Graph Neural Network.

Operational Rules & Constraints

Mapping: Define mapping dictionaries for all categorical fields (e.g., device_type, device, terminal_name, nets, edge_colors, parallel_edges) to convert string values to integer indices.
Embedding Layers: Initialize nn.Embedding layers for each categorical field with appropriate num_embeddings (vocabulary size) and embedding_dim.
Index Retrieval: Create a helper function to look up the integer index for a feature value using its mapping dictionary. Handle unknown values if necessary.
Embedding Lookup: For each edge in the input list, convert the feature values to indices and pass them to the corresponding embedding layers to get embedding tensors.
Concatenation Logic: a. Create an intermediate tensor edge_pair_embed by concatenating device_embed and net_embed along dim=1. b. Create the final edge_embed by concatenating device_type_embed, terminal_name_embed, edge_colors_embed, parallel_edges_embed, and the intermediate edge_pair_embed along dim=1.
Output: Return a list of the final concatenated embedding tensors.

Anti-Patterns

Do not pass raw string values directly to torch.tensor for embedding lookups.
Do not concatenate tensors with mismatched dimensions.
Do not omit the intermediate edge_pair_embed step inside the final concatenation.

Triggers

generate edge embeddings for GNN
convert categorical edge features to tensors
create embedding function for graph edges
encode string edge features for neural network