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74 lines
3.4 KiB
Markdown
74 lines
3.4 KiB
Markdown
# HyperNetworkCombiner: Conditional Feature Processing
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> **Note:** This example requires PR #4092 to be merged into Ludwig, or `pip install ludwig` >= 0.14.
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[](https://colab.research.google.com/github/ludwig-ai/ludwig/blob/main/examples/hypernetwork/hypernetwork.ipynb)
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## What the HyperNetworkCombiner does differently
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Most combiners — including the default `concat` combiner — treat all input features
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symmetrically: they encode each feature independently and then merge the resulting
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vectors (by concatenation, attention, or summation). The merged representation is
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the same *kind* of computation regardless of what any individual feature says.
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The `hypernetwork` combiner breaks this symmetry. One feature, called the
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**conditioning feature**, is fed through a small *hyper-network* that generates the
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weight matrices and biases of the fully-connected layers that process all other features.
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In other words, the conditioning feature does not just *contribute* to the prediction —
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it *rewrites the transformation* applied to every other feature before prediction happens.
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This is based on **HyperFusion** (arXiv 2403.13319, 2024).
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```
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sensor_type ──► HyperNetwork ──► generates weights W, b
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│
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sensor_a ─────────────────────► FC(W, b) ──► combined
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sensor_b ─────────────────────► FC(W, b) ──► repr.
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sensor_c ─────────────────────► FC(W, b) ──►
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```
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Contrast with concat:
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```
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sensor_type ──► encoder ──┐
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sensor_a ──► encoder ──┤
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sensor_b ──► encoder ──┼──► concat ──► FC ──► output
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sensor_c ──► encoder ──┘
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```
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With `concat`, the network learns *after* combining to react to different sensor types.
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With `hypernetwork`, the combination itself is conditioned on sensor type.
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## When to use it
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Use the `hypernetwork` combiner when:
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- One feature is a **context** or **mode** that fundamentally changes how other
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features should be interpreted (sensor type, device class, environment, language).
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- The relationship between inputs and the target changes qualitatively across groups,
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not just quantitatively.
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- You have enough training data to learn the per-context transformations (at minimum a
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few hundred samples per conditioning category).
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Stick with `concat` when:
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- All input features contribute on equal footing.
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- The dataset is small (the hyper-network adds parameters).
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- Interpretability of the encoding step is important and you want a fixed transformation.
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## Files
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| File | Description |
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| -------------------------- | ------------------------------------------------------------------------- |
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| `hypernetwork.ipynb` | End-to-end walkthrough with synthetic sensor data |
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| `config_concat.yaml` | Baseline concat config |
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| `config_hypernetwork.yaml` | HyperNetworkCombiner config |
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| `train_hypernetwork.py` | Standalone script — generates data, trains both models, prints comparison |
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## Quick start
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```bash
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pip install "ludwig>=0.14"
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python train_hypernetwork.py
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```
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