--- a
+++ b/examples/load_and_save_models.py
@@ -0,0 +1,128 @@
+import os
+os.environ['PYTHONHASHSEED']=str(2020)
+
+import random
+random.seed(2020)
+
+from os.path import abspath
+from os.path import split
+
+from simdeep.simdeep_boosting import SimDeepBoosting
+from simdeep.simdeep_utils import save_model
+from simdeep.simdeep_utils import load_model
+
+
+def test_instance():
+    """
+    example of SimDeepBoosting
+    """
+    PATH_DATA = '{0}/../examples/data/'.format(split(abspath(__file__))[0])
+
+    #Input file
+    TRAINING_TSV = {'RNA': 'rna_dummy.tsv', 'METH': 'meth_dummy.tsv'}
+    SURVIVAL_TSV = 'survival_dummy.tsv'
+
+    PROJECT_NAME = 'TestProject'
+    SEED = 3
+    nb_it = 2 # Number of models to be built
+    nb_threads = 2 # Number of processes to be used to fit individual survival models
+
+    ################ AUTOENCODER PARAMETERS ################
+    EPOCHS = 10
+    ## Additional parameters for the autoencoders can be defined, see config.py file for details
+    # LEVEL_DIMS_IN = [250]
+    # LEVEL_DIMS_OUT = [250]
+    # LOSS = 'binary_crossentropy'
+    # OPTIMIZER = 'adam'
+    # ACT_REG = 0
+    # W_REG = 0
+    # DROPOUT = 0.5
+    # DATA_SPLIT = 0
+    # ACTIVATION = 'tanh'
+    #########################################################
+
+    ################ ADDITIONAL PARAMETERS ##################
+    # PATH_TO_SAVE_MODEL = '/home/username/deepprog'
+    # PVALUE_THRESHOLD = 0.01
+    # NB_SELECTED_FEATURES = 10
+    # STACK_MULTI_OMIC = False
+    #########################################################
+
+    from sklearn.preprocessing import RobustScaler
+    norm = {
+            'CUSTOM': RobustScaler,
+    }
+
+    boosting = SimDeepBoosting(
+        nb_threads=nb_threads,
+        nb_it=nb_it,
+        split_n_fold=3,
+        survival_tsv=SURVIVAL_TSV,
+        training_tsv=TRAINING_TSV,
+        path_data=PATH_DATA,
+        project_name=PROJECT_NAME,
+        path_results=PATH_DATA,
+        epochs=EPOCHS,
+        seed=SEED,
+        normalization=norm,
+        cluster_method='mixture',
+        use_autoencoders=True,
+        feature_surv_analysis=True,
+        distribute=False
+        # stack_multi_omic=STACK_MULTI_OMIC,
+        # level_dims_in=LEVEL_DIMS_IN,
+        # level_dims_out=LEVEL_DIMS_OUT,
+        # loss=LOSS,
+        # optimizer=OPTIMIZER,
+        # act_reg=ACT_REG,
+        # w_reg=W_REG,
+        # dropout=DROPOUT,
+        # data_split=DATA_SPLIT,
+        # activation=ACTIVATION,
+        # path_to_save_model=PATH_TO_SAVE_MODEL,
+        # pvalue_threshold=PVALUE_THRESHOLD,
+        # nb_selected_features=NB_SELECTED_FEATURES,
+    )
+
+    boosting.fit()
+    save_model(boosting, "./test_saved_model")
+
+    del boosting
+
+    boosting = load_model("TestProject", "./test_saved_model")
+
+    boosting.predict_labels_on_full_dataset()
+
+    boosting.compute_clusters_consistency_for_full_labels()
+    boosting.evalutate_cluster_performance()
+    boosting.collect_cindex_for_test_fold()
+    boosting.collect_cindex_for_full_dataset()
+
+    boosting.compute_feature_scores_per_cluster()
+    boosting.write_feature_score_per_cluster()
+    boosting.collect_number_of_features_per_omic()
+    boosting.compute_pvalue_for_merged_test_fold()
+
+    boosting.load_new_test_dataset(
+        {'RNA': 'rna_dummy.tsv'}, # OMIC file of the test set. It doesnt have to be the same as for training
+        'dummy', # Name of the test test to be used
+        'survival_dummy.tsv', # Survival file of the test set Optional
+    )
+
+    boosting.predict_labels_on_test_dataset()
+    boosting.compute_c_indexes_for_test_dataset()
+    boosting.compute_clusters_consistency_for_test_labels()
+
+    boosting.load_new_test_dataset(
+        {'METH': 'meth_dummy.tsv'}, # OMIC file of the second test set.
+        'dummy_METH', # Name of the second test test
+        'survival_dummy.tsv', # Survival file of the test set Optional
+    )
+
+    boosting.predict_labels_on_test_dataset()
+    boosting.compute_c_indexes_for_test_dataset()
+    boosting.compute_clusters_consistency_for_test_labels()
+
+
+if __name__ == '__main__':
+    test_instance()