GitLab

Improvements to AstroNet and add AstroWaveNet

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PiperOrigin-RevId: 215014489

Add AstroWaveNet model, a generative model of astronomical light curves. AstroWaveNet's hidden states can be used in a semi-supervised fashion for downstream tasks like finding exoplanets.

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Factor out evaluate(), continuous_eval(), and continuous_train_and_eval() from estimator_util.py into estimator_runner.py.

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1. reshard_arrays(xs, ys): Reshards arrays in xs to match the lengths of arrays in ys.
2. uniform_cadence_light_curve(): Combines data into a single light curve with uniform cadence numbers.

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Remove the constant LONG_CADENCE_TIME_DELTA_DAYS as this is not strictly constant between light curves (although it is very close).

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This applies mainly to scrambled data (NaN time values typically come with NaN flux values, which are removed anyway, but scrambing decouples NaN time values from NaN flux values).

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Update configurations with label mapping for simulated data and new batch_size and learning_rate from vizier studies.

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Ensure consistent argument order, change NaN processing to remove NaN times as well, and do so before scrambling

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Modify processing pipeline to enable generation of scrambled lightcurves. Fix bugs to enable generation of inverted lightcurves.

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Inversion should be done *after* fitting a normalization curve to the stellar variability and dividing it away. Doing it before normalizing may result in unintentionally reversing the inversion. Therefore we remove the option to do it at this point.

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* move flagfile into the cache_dir

* remove duplicate code

* delint

* Use data_dir instead of flags.FLAGS.data_dir in data_preprocessing.py.

* Use data_dir instead of flags.FLAGS.data_dir in data_preprocessing.py.

* Replace multiprocess pool with popen_helper.get_pool() in data_preprocessing.

* Use data_dir instead of flags.FLAGS.data_dir in data_preprocessing.py.

* Use data_dir instead of flags.FLAGS.data_dir in data_preprocessing.py.

Allow data async generation to be run as a standalone job rather than a subprocess.

Add comments, exit async process after waiting for flagfile for too long and make directory for data_dir in case it does not exist.

Prevents TypeError:'dict_keys' object does not support indexing

bug fix and python3 compatability

fixed bug and introduced python3 compatability

* Add --use_synthetic_data option to NCF.

* Add comment to _SYNTHETIC_BATCHES_PER_EPOCH

* Fix test

* Hopefully fix lint issue