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bhaprayan avatar bscot avatar eteq avatar jakehlee avatar wkiri avatar

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demud's Issues

Sweep k values for DEMUD

Deploy parallel jobs on halo/aurora to compute AUC over a variety of k values (ImageNet + stonefly9).

  • Request supercomputer access for Jake

Demud not defined

Hello, I'm trying to run demud on Spyder on python 3 but it's not defined.

Make "static" mode more efficient

Currently a static SVD result is achieved by simply not updating the SVD model at each iteration. However, the remaining pool of unselected items gets re-scored and ranked each time, even though this ranking will not change.

Suggestion: modify code so "static" path bypasses/shortcuts this iteration. A single ranking of the items should be enough.

Update ENVI data processing to work with user-modified files

Abby F. has an example ENVI file for which DEMUD isn’t reading band names correctly (assumption that band number is followed by ':' is incorrect). It also doesn't read the data correctly - or the data format is wrong - it gets about half of the expected data values. She will send me both original and modified files (and .hdr files) so I can debug this.

Dependency errors due to NIMS support

Summary:

Fresh installation with master throws an error due to nims_data dependency in dataset_envi.py.

Reproduction:

  1. git clone
  2. demud --make-config
  3. run demud on anything

Description:

5dd4769 added

DEMUD/demud/demud.py

Lines 723 to 726 in 5dd4769

if (isinstance(ds, SegENVIData) or
isinstance(ds, ENVIData)):
ds.write_RGB(os.path.join(outdir,
'%s-rgb-viz.png' % ds.name.split('-')[1]))

now requiring

DEMUD/demud/demud.py

Lines 38 to 39 in 5dd4769

from dataset_envi import ENVIData
from dataset_envi import SegENVIData

e0fa4ef added

from nims_data import NimsQube

where nims_data seems to be a “non-standard” dependency

Workaround:

comment out line 28 from dataset_envi.py for any non-ENVI/NIMS run:

from nims_data import NimsQube

Order of features changes selections

e.g. for dataset_des.py on Y3_GOLD_2_2_subsampled_filtered.npy. I get the same selection 0, but after that different selections, when swapping the order of the first two features.

Find out why and whether this is desired behavior.

Switch default algorithm for --increm

Two methods have been implemented for incrementally updating the SVD model: increm-ross and increm-brand. --increm currently defaults to --svdmethod=increm-ross. However, comparing the discovery results from these methods and the full method (which recomputes the SVD model from scratch at each iteration) shows that the results of increm-ross significantly deviate from full, whereas increm-brand performs similarly to full.

Therefore, --increm should default to --svdmethod=increm-brand, and the message for --svdmethods should be updated as such.

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