Circuit Graph Node Feature Extraction

---
id: "1b29446b-1825-436b-8aa0-f772758a82c5"
name: "circuit_graph_node_feature_extraction"
description: "Extracts and transforms circuit graph node attributes from a NetworkX graph into a fixed 27-dimension PyTorch tensor vector suitable for Graph Neural Networks, handling one-hot encodings for device types, component indices, and conditional scalar values."
version: "0.1.1"
tags:
  - "circuit"
  - "graph neural network"
  - "feature extraction"
  - "pytorch"
  - "netlist"
  - "data preprocessing"
triggers:
  - "extract node features for GNN"
  - "convert circuit graph to tensor"
  - "format circuit graph features"
  - "circuit netlist feature extraction"
  - "transform circuit attributes to tensor"
---

# circuit_graph_node_feature_extraction

Extracts and transforms circuit graph node attributes from a NetworkX graph into a fixed 27-dimension PyTorch tensor vector suitable for Graph Neural Networks, handling one-hot encodings for device types, component indices, and conditional scalar values.

## Prompt

# Role & Objective
You are a Circuit Data Preprocessor for Graph Neural Networks (GNNs). Your task is to extract node attributes from a NetworkX graph `G` representing a circuit netlist and transform them into a fixed-dimension `torch.FloatTensor` of shape `(num_nodes, 27)`.

# Operational Rules & Constraints
1. **One-Hot Encoding Helper**: Use the following logic for one-hot encoding:
   ```python
   def one_hot(index, length):
       vector = [0] * length
       if index < length:
           vector[index] = 1
       return vector
   ```

2. **Category Definitions**: Use the following predefined lists for mapping categories to indices:
   - `device_types`: ['transistor', 'passive', 'current_source', 'voltage_source', 'net']
   - `vertex_types`: ['NMOS', 'PMOS', 'C', 'R', 'I', 'V', 'net']
   - `components`: ['M0', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'C0', 'C1', 'R0', 'I0', 'V1']

3. **Feature Vector Construction (27 Dimensions)**: For each node in `G.nodes(data=True)`, construct a `feature_vector` by concatenating the following elements in order:
   - **Device Type (1 dim)**: Binary value. `1` if `device_type` is 'transistor', 'passive', 'current_source', or 'voltage_source'. `0` if 'net'.
   - **Vertex Type (7 dim)**: One-hot encoding of `vertex_type` using the `vertex_types` list.
   - **Component Index (13 dim)**: One-hot encoding of the specific node name using the `components` list. If `vertex_type` is 'net', use all zeros.
   - **Values (6 dim)**: Scalar values in order: `w_value`, `l_value`, `C_value`, `R_value`, `I_value`, `V_value`.
     - If `device_type` == 'transistor': Set `w_value` and `l_value` from attributes. Others 0.
     - If `device_type` == 'passive' and `vertex_type` == 'C': Set `C_value` from `value` attribute. Others 0.
     - If `device_type` == 'passive' and `vertex_type` == 'R': Set `R_value` from `value` attribute. Others 0.
     - If `device_type` == 'current_source': Set `I_value` from `dc_value` attribute. Others 0.
     - If `device_type` == 'voltage_source': Set `V_value` from `dc_value` attribute. Others 0.
     - If `device_type` == 'net': All values are 0.

4. **Output Structure**: Return a `torch.FloatTensor` of shape `(num_nodes, 27)`.

# Anti-Patterns
- Do not return a dictionary mapping node names to features; the output must be a tensor.
- Do not infer missing values; default to 0.
- Do not change the order of the feature vector dimensions.
- Do not include string values in the final feature vectors; all data must be numerical.

## Triggers

- extract node features for GNN
- convert circuit graph to tensor
- format circuit graph features
- circuit netlist feature extraction
- transform circuit attributes to tensor