Add Feature Extraction Support for API Classifiers #77
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🔍 Existing Issues For ReviewYour pull request is modifying functions with the following pre-existing issues: 📄 File: trapdata/api/models/classification.py
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mihow
reviewed
Apr 26, 2025
| @@ -36,7 +36,8 @@ pyobjus = [ | |||
| { version = "^1.2.1", platform = "darwin" }, | |||
| { version = "^1.2.1", platform = "linux" }, | |||
| ] | |||
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| plotly = "^5.21.0" | |||
| scikit-learn = "^1.3.0" | |||
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I think we should make these optional dependencies and just use numpy in the tests. unless we need to use them in the core app.
[tool.poetry.extras]
dev = ["plotly", "scikit-learn"]
mihow
reviewed
Apr 26, 2025
mihow
reviewed
Apr 26, 2025
| @@ -287,6 +287,16 @@ def get_model(self): | |||
| model.eval() | |||
| return model | |||
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| def get_features(self, batch_input: torch.Tensor) -> torch.Tensor: | |||
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Nice work on this method of extracting features! It seems more flexible than our current feature extractor. Perhaps we should add a comment in both feature extractors that the other one exists. And eventually update the old one to use this code.
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Description
This PR adds support for returning model feature vectors (embeddings) alongside classification results in the Data Companion API.
The classification pipeline now supports returning a vector embedding per classification, derived from the classification model backbone.
The changes are fully backward-compatible for models that do not implement custom get_features(), as they will fallback to returning
Nonefrom the base class.Related Issues
#752
Screenshots
Detection features clustering visualization using K-means + PCA
