def test_store_subsampled_features(self):
results, idval = store_feature_results.store_subsampled_feature_results(
self.F, self.meta, self.decoder_model, self.fs, self.feature_inds,
self.additional_info)
desired_split = decoder_models.ImageSet1_inds
test_split = np.array(results['splits'][0][0]['test'])
new_order = np.argsort(test_split)
test_split = test_split[new_order]
self.assertItemsEqual(desired_split, test_split)
self.ids.append(idval)
def test_store_subsampled_features(self):
results, idval = store_feature_results.store_subsampled_feature_results(self.F, self.meta,
self.decoder_model,
self.fs,
self.feature_inds,
self.additional_info)
desired_split = decoder_models.ImageSet1_inds
test_split = np.array(results['splits'][0][0]['test'])
new_order = np.argsort(test_split)
test_split = test_split[new_order]
self.assertItemsEqual(desired_split, test_split)
self.ids.append(idval)
import sys
import gridfs
import pymongo as pm
feature_name = sys.argv[1]
decoder_model_name = sys.argv[3] # Can make this an option later
feature_split = [int(ind) for ind in sys.argv[2].split(',')]
# Load feature from name
features, meta = feature_loader.get_features_by_name(feature_name)
#Load decoder model from name
decoder_model = decoder_models.get_decoder_model_by_name(decoder_model_name)
fs = store_feature_results.get_gridfs(decoder_model_name=decoder_model_name, # Decide where to store things
feature_name=feature_name)
additional_info = {'feature_split': feature_split}
store_feature_results.store_subsampled_feature_results(features, meta,
decoder_model, fs,
feature_split,
additional_info)
__author__ = 'ardila'
import sys
import gridfs
import pymongo as pm
feature_name = sys.argv[1]
decoder_model_name = sys.argv[3] # Can make this an option later
feature_split = [int(ind) for ind in sys.argv[2].split(',')]
# Load feature from name
features, meta = feature_loader.get_features_by_name(feature_name)
#Load decoder model from name
decoder_model = decoder_models.get_decoder_model_by_name(decoder_model_name)
fs = store_feature_results.get_gridfs(
decoder_model_name=decoder_model_name, # Decide where to store things
feature_name=feature_name)
additional_info = {'feature_split': feature_split}
store_feature_results.store_subsampled_feature_results(features, meta,
decoder_model, fs,
feature_split,
additional_info)
