PHATE #210
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PHATE #210
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edyoshikun
commented
Nov 20, 2024
edyoshikun
commented
- Adding support for PHATE
- Adding reviewed Alishba's scripts for ALFI.
…this does not work.
and modifying previous to match same input args
- removing unecessary calls to ALFI script - simplifying code to re-use functions
adding docstring
ziw-liu
reviewed
Nov 21, 2024
|
@edyoshikun , can we add the calculation and storage of PHATE in the predictions in this PR? |
ziw-liu
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Dec 15, 2024
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and modifying previous to match same input args
This reverts commit 464d4c9.
* explicit target shape argument in the HCS data module * update docstring * update test cases
* initial demo * using the predict_step * modifying paths to chkpt and example pngs * updating gradio as the one on Huggingface
ziw-liu
reviewed
Dec 20, 2024
edyoshikun
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Dec 23, 2024
* translation: fix validation loss aggregation (#202) * exposing prefetch and persistent worker (#203) * metrics for dynamic, smoothness and docstrings * updated metrics and plots for distance * fixed CI test cases * nexnt loss prototype * fix bug with z_scale_range in hcs datamodule. If the value is an int this does not work. * exclude the negative pair from dataloader and forward pass * adding option using pytorch-metric-learning implementation and modifying previous to match same input args * removing our implementation of NTXentLoss and using pytorch metric * ruff * prototype for phate and umap plot * - proofreading the calculations - removing unecessary calls to ALFI script - simplifying code to re-use functions * methods to rank nearest neighbors in embeddings * example script to plot state change of a single track * test using scaled features * phate embeddings * removing dataframe from the compute_phate adding docstring * adding phate to the prediction writer and moving it as dependency. * changing the phate defaults in the prediction writer. * ruff * fixing bug in phate in predict writer * adding code for measuring the smoothness * cleanup to run on triplet and ntxent * fix plots for smoothnes * nexnt loss prototype * exclude the negative pair from dataloader and forward pass * adding option using pytorch-metric-learning implementation and modifying previous to match same input args * removing our implementation of NTXentLoss and using pytorch metric * ruff * remove blank line diff * remove blank line diff * simplying the engine * explicit target shape argument in the HCS data module * Revert "explicit target shape argument in the HCS data module" This reverts commit 464d4c9. * Explicit target shape argument in the HCS data module (#212) * explicit target shape argument in the HCS data module * update docstring * update test cases * Gradio example (#158) * initial demo * using the predict_step * modifying paths to chkpt and example pngs * updating gradio as the one on Huggingface * adding configurable phate arguments via config * script to recompute phate and overwrite the previous phate data * ruff * solving redundancies * modularizing the smoothness * removing redundant _fit_phate() * ruff --------- Co-authored-by: Ziwen Liu <[email protected]> Co-authored-by: Alishba Imran <[email protected]> Co-authored-by: Ziwen Liu <[email protected]>
mattersoflight
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Dec 23, 2024
* nexnt loss prototype * fix bug with z_scale_range in hcs datamodule. If the value is an int this does not work. * exclude the negative pair from dataloader and forward pass * adding option using pytorch-metric-learning implementation and modifying previous to match same input args * removing our implementation of NTXentLoss and using pytorch metric * ruff * remove blank line diff * remove blank line diff * simplying the engine * PHATE (#210) * translation: fix validation loss aggregation (#202) * exposing prefetch and persistent worker (#203) * metrics for dynamic, smoothness and docstrings * updated metrics and plots for distance * fixed CI test cases * nexnt loss prototype * fix bug with z_scale_range in hcs datamodule. If the value is an int this does not work. * exclude the negative pair from dataloader and forward pass * adding option using pytorch-metric-learning implementation and modifying previous to match same input args * removing our implementation of NTXentLoss and using pytorch metric * ruff * prototype for phate and umap plot * - proofreading the calculations - removing unecessary calls to ALFI script - simplifying code to re-use functions * methods to rank nearest neighbors in embeddings * example script to plot state change of a single track * test using scaled features * phate embeddings * removing dataframe from the compute_phate adding docstring * adding phate to the prediction writer and moving it as dependency. * changing the phate defaults in the prediction writer. * ruff * fixing bug in phate in predict writer * adding code for measuring the smoothness * cleanup to run on triplet and ntxent * fix plots for smoothnes * nexnt loss prototype * exclude the negative pair from dataloader and forward pass * adding option using pytorch-metric-learning implementation and modifying previous to match same input args * removing our implementation of NTXentLoss and using pytorch metric * ruff * remove blank line diff * remove blank line diff * simplying the engine * explicit target shape argument in the HCS data module * Revert "explicit target shape argument in the HCS data module" This reverts commit 464d4c9. * Explicit target shape argument in the HCS data module (#212) * explicit target shape argument in the HCS data module * update docstring * update test cases * Gradio example (#158) * initial demo * using the predict_step * modifying paths to chkpt and example pngs * updating gradio as the one on Huggingface * adding configurable phate arguments via config * script to recompute phate and overwrite the previous phate data * ruff * solving redundancies * modularizing the smoothness * removing redundant _fit_phate() * ruff --------- Co-authored-by: Ziwen Liu <[email protected]> Co-authored-by: Alishba Imran <[email protected]> Co-authored-by: Ziwen Liu <[email protected]> * renaming cross_dissimilairy with pairwaise_distance_matrix --------- Co-authored-by: Ziwen Liu <[email protected]> Co-authored-by: Alishba Imran <[email protected]> Co-authored-by: Ziwen Liu <[email protected]>
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