def main(pickle_adr,model_adr,save_adr,verbose):
# Pickles of unlabelled glitches have already saved in this address
pickle_adr += '/'
# the path where the trained is saved there
model_adr += '/'
# the path where the .csv files of the results are saved there
save_adr += '/'
if not os.path.exists(save_adr):
if verbose:
print ('making... ' + save_adr)
os.makedirs(save_adr)
np.random.seed(1986) # for reproducibility
img_rows, img_cols = 47, 57
nb_classes = 20
# load a model and weights
if verbose:
print ('Retrieving the trained ML classifier')
load_folder = model_adr
f = gzip.open(load_folder + '/model.pklz', 'rb')
json_string = cPickle.load(f)
f.close()
final_model = model_from_json(json_string)
final_model.load_weights(load_folder + '/model_weights.h5')
final_model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
if verbose:
print ('Scoring unlabelled glitches')
# reading all 4 duration pickles
unlabelled_pickles = ['img_1.0*', 'img_2.0*', 'img_4.0*', 'img_5.0*'] # adding option to do in in alphabetical order
# read duration 1 second
dataset_test_unlabelled_1 = load_dataset_unlabelled_glitches(glob.glob(pickle_adr + unlabelled_pickles[0])[0],verbose)
[test_set_unlabelled_x_1, test_set_unlabelled_y_1, test_set_unlabelled_name_1] = dataset_test_unlabelled_1
test_set_unlabelled_x_1 = test_set_unlabelled_x_1.reshape(-1, 1, img_rows, img_cols)
dataset_test_unlabelled_2 = load_dataset_unlabelled_glitches(glob.glob(pickle_adr + unlabelled_pickles[1])[0],verbose)
[test_set_unlabelled_x_2, test_set_unlabelled_y_2, test_set_unlabelled_name_2] = dataset_test_unlabelled_2
test_set_unlabelled_x_2 = test_set_unlabelled_x_2.reshape(-1, 1, img_rows, img_cols)
dataset_test_unlabelled_3 = load_dataset_unlabelled_glitches(glob.glob(pickle_adr + unlabelled_pickles[2])[0],verbose)
[test_set_unlabelled_x_3, test_set_unlabelled_y_3, test_set_unlabelled_name_3] = dataset_test_unlabelled_3
test_set_unlabelled_x_3 = test_set_unlabelled_x_3.reshape(-1, 1, img_rows, img_cols)
dataset_test_unlabelled_4 = load_dataset_unlabelled_glitches(glob.glob(pickle_adr + unlabelled_pickles[3])[0],verbose)
[test_set_unlabelled_x_4, test_set_unlabelled_y_4, test_set_unlabelled_name_4] = dataset_test_unlabelled_4
test_set_unlabelled_x_4 = test_set_unlabelled_x_4.reshape(-1, 1, img_rows, img_cols)
if verbose:
print('The number of unlabelled glitches is: ', test_set_unlabelled_x_1.shape[0])
concat_test_unlabelled = square_early_concatenate_feature(test_set_unlabelled_x_1, \
test_set_unlabelled_x_2, test_set_unlabelled_x_3, test_set_unlabelled_x_4,[img_rows, img_cols])
score3_unlabelled = final_model.predict_proba(concat_test_unlabelled, verbose=0)
name_array_unlabelled = np.array([test_set_unlabelled_name_1.tolist()])
name_array_unlabelled = np.transpose(name_array_unlabelled)
dw = np.concatenate((name_array_unlabelled, score3_unlabelled), axis=1)
return dw[0],np.argmax(score3_unlabelled[0])
