Investigate predictions on "swap" synthetic test dataset

[SteveOv]

Investigate the phenomenon shown below, when we test against a synthetic test dataset where instances are swapped if the original secondary eclipse is found to be deeper.

All instances	Transiting instances

[SteveOv]

Added a swapped field to saved CSVs with 81a257d

[SteveOv]

Can confirm that this is definitely down to the instances that have been swapped.

Swapped instances	Non-swapped instances

[SteveOv]

By combining the swapped and transiting criteria we see that the majority are swapped and transiting.

Swapped & transiting instances	swapped & non-transiting instances

[SteveOv]

Doesn't completely resolve the issue, but I've found that I've been handling the change in bP and bS when switching components incorrectly. These need to be recalculated as they must relate to the newly assigned star A

[SteveOv]

Tried training models on a datasets with swap enabled. Invariably, these improved the results with the synthetic set with swap enabled at the expense of these without it, however the net result was significantly worse predictions.

The following shows the predictions for k against both "swapped" and "non-swapped" synthetic-mist-tess-datasets with a model trained on s 100k dataset with swapped instances (without additional restrictions on k, J or qphot);

synth test dataset with swap	synth test dataset without swap

For the "swap" model (trained on 100k train/val instances without swap):

test dataset	all instances	transiting	non-transiting
synth test dataset with swap (k<=10)	0.060 041	0.124 873	0.042 900
synth test dataset without swap	0.061 276	0.092 278	0.053 035
formal test dataset (effectively with swap)	0.064 847	0.107 058	0.0530122

For the control model (trained on 100k train/val instances without swap):

test dataset	all instances	transiting	non-transiting
synth test dataset with swap (k<=10)	0.071 529	0.179 780	0.042 909
synth test dataset without swap	0.040 515	0.063 774	0.034 333
formal test dataset (effectively with swap)	0.050 801	0.077 074	0.043 503

[SteveOv]

Returning to this with models trained with the mags feature centred on the midpoint between the eclipses and roll (agumentation) <= 512 bins.