Bibliographic record
Abstract
This release features two new types of lifting functions: radial basis functions, and random Fourier features. Click the links for examples, or check them out on Binder! You can now also use almost any scikit-learn regressor as a backend for EDMD with EdmdMeta. You can find a cool example of sparse regression with the lasso here. Finally, two quality-of-life changes are introduced in this update. You can access your lifting function feature names with KoopmanLiftingFn.get_feature_names_out(), and you can quickly plot Koopman predictions and Koopman operator properties with a bunch of plot_*() methods scattered throughout the library. See below for more details. Note that in this release, we are dropping official Python 3.7 support, though almost all features should still work. Full changelog: https://github.com/decargroup/pykoop/compare/v1.0.5...v1.1.0 New features Added radial basis function (RBF) lifting functions in RbfLiftingFn, along with several ways to choose centers (https://github.com/decargroup/pykoop/pull/103) Added random Fourier feature (RFF) lifting functions in KernelApproxLiftingFn, along with other kernel approximations (https://github.com/decargroup/pykoop/pull/110) Added constant lifting function in ConstantLiftingFn (https://github.com/decargroup/pykoop/pull/85) Added support for scikit-learn linear regressors in EdmdMeta (https://github.com/decargroup/pykoop/pull/92) Added support for feature name tracking as strings in KoopmanLiftingFn.get_feature_names_in() and KoopmanLiftingFn.get_feature_names_out(). If you pass in a pandas.DataFrame, then pykoop can take the feature names from there (https://github.com/decargroup/pykoop/pull/75) Added easy plotting helpers in KoopmanLiftingFn.plot_lifted_trajectory(), KoopmanRegressor.plot_bode(), KoopmanRegressor.plot_eigenvalues(), KoopmanRegressor.plot_koopman_matrix(), KoopmanRegressor.plot_svd(), KoopmanPipeline.plot_predicted_trajectory(), KoopmanPipeline.plot_bode(), KoopmanPipeline.plot_eigenvalues(), KoopmanPipeline.plot_koopman_matrix(), and KoopmanPipeline.plot_svd() (https://github.com/decargroup/pykoop/pull/83) Added example_data_pendulum() and example_data_duffing(). Bug fixes Fixed bug where predict_trajectory indexing was wrong when relift_state=false (https://github.com/decargroup/pykoop/pull/112) Fixed Binder package versions (https://github.com/decargroup/pykoop/pull/108)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.420 | 0.547 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".