#NEWLINE# def test_classification_2classes_small_jp( ) : #NEWLINE# #INDENT# ( X , y ) = make_classification ( n_samples = 1000 , n_features = 10 , n_classes = 2 , n_clusters_per_class = 1 , random_state = 0 ) #NEWLINE# X = pd . DataFrame ( X ) #NEWLINE# y = pd . Series ( y ) #NEWLINE# cls = MALSS ( 'classification' , lang = 'jp' ) . fit ( X , y , 'test_classification_2classes_small_jp' ) #NEWLINE# cls . generate_module_sample ( ) #NEWLINE# from sklearn.metrics import f1_score #NEWLINE# pred = cls . predict ( y ) #NEWLINE# print ( f1_score ( y , pred , average = None ) ) #NEWLINE# assert ( len ( cls . algorithms ) == 6 ) #NEWLINE# assert ( cls . algorithms [ 0 ] . best_score is not None )