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)