diff --git a/lang/fr/gklearn/tests/test_median_preimage_generator.py b/lang/fr/gklearn/tests/test_median_preimage_generator.py
new file mode 100644
index 0000000..a376c7a
--- /dev/null
+++ b/lang/fr/gklearn/tests/test_median_preimage_generator.py
@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan 14 15:39:29 2020
+
+@author: ljia
+"""
+import multiprocessing
+import functools
+from gklearn.preimage.utils import generate_median_preimages_by_class
+
+		
+def test_median_preimage_generator():
+	"""MAO, Treelet, using CONSTANT, symbolic only.
+	"""
+	from gklearn.utils.kernels import polynomialkernel
+	# set parameters.
+	ds_name = 'MAO' #
+	mpg_options = {'fit_method': 'k-graphs',
+				   'init_ecc': [4, 4, 2, 1, 1, 1], #
+				   'ds_name': ds_name,
+				   'parallel': True, # False
+				   'time_limit_in_sec': 0,
+				   'max_itrs': 3, # 
+				   'max_itrs_without_update': 3,
+				   'epsilon_residual': 0.01,
+				   'epsilon_ec': 0.1,
+				   'verbose': 2}
+	pkernel = functools.partial(polynomialkernel, d=4, c=1e+7)
+	kernel_options = {'name': 'Treelet', #
+				      'sub_kernel': pkernel,
+ 					  'parallel': 'imap_unordered', 
+                      # 'parallel': None, 
+					  'n_jobs': multiprocessing.cpu_count(),
+					  'normalize': True,
+					  'verbose': 2}
+	ged_options = {'method': 'IPFP',
+				   'initialization_method': 'RANDOM', # 'NODE'
+				   'initial_solutions': 1, # 1
+				   'edit_cost': 'CONSTANT', # 
+				   'attr_distance': 'euclidean',
+				   'ratio_runs_from_initial_solutions': 1,
+				   'threads': multiprocessing.cpu_count(),
+				   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
+	mge_options = {'init_type': 'MEDOID',
+				   'random_inits': 10,
+				   'time_limit': 600,
+				   'verbose': 2,
+				   'refine': False}
+	save_results = True
+	dir_save = ds_name + '.' + kernel_options['name'] + '.symb.pytest/'
+	irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # 
+	edge_required = False #
+	
+	# print settings.
+	print('parameters:')
+	print('dataset name:', ds_name)
+	print('mpg_options:', mpg_options)
+	print('kernel_options:', kernel_options)
+	print('ged_options:', ged_options)
+	print('mge_options:', mge_options)
+	print('save_results:', save_results)
+	print('irrelevant_labels:', irrelevant_labels)
+	print()
+	
+	# generate preimages.
+	for fit_method in ['k-graphs', 'expert', 'random']:
+		print('\n-------------------------------------')
+		print('fit method:', fit_method, '\n')
+		mpg_options['fit_method'] = fit_method
+		try:
+			generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=range(0, 4))
+		except Exception as exception:
+			assert False, exception
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