original_docstring: 
          split the data into rain and test for the true edges and the false one.
          :param ratio: determine the train size.
          :return: THe split data
          
docstring: split the data into rain and test for the true edges and the false one.
docstring_tokens: split the data into rain and test for the true edges and the false one .
code: def split_data(X_true, X_false, Y_true_edge, Y_false_edge, ratio):
          """
          split the data into rain and test for the true edges and the false one.
          :param ratio: determine the train size.
          :return: THe split data
          """
          X_train_true, X_test_true, Y_train_true_edge, \
          Y_test_true_edge = sk_ms.train_test_split(X_true, Y_true_edge, test_size=1 - ratio)
          X_train_false, X_test_false, Y_train_false_edge, \
          Y_test_false_edge = sk_ms.train_test_split(X_false, Y_false_edge, test_size=1 - ratio)
          true_edges_test_source = Y_test_true_edge.T[2].reshape(-1, 1)
          true_edges_test_target = Y_test_true_edge.T[3].reshape(-1, 1)
          X_train, X_test, Y_train_edge, Y_test_edge = np.concatenate((X_train_true, X_train_false), axis=0), \
                                                       np.concatenate((X_test_true, X_test_false), axis=0), \
                                                       np.concatenate((Y_train_true_edge, Y_train_false_edge), axis=0), \
                                                       np.concatenate((Y_test_true_edge, Y_test_false_edge), axis=0)
          Y_train = np.array([Y_train_edge.T[0].reshape(-1, 1), Y_train_edge.T[1].reshape(-1, 1)]).T.reshape(-1,
                                                                                                             2).astype(
              int)
          true_edges_test = np.array([true_edges_test_source, true_edges_test_target]).T[0]
          return X_train, Y_train, true_edges_test
code_tokens: def split_data ( X_true , X_false , Y_true_edge , Y_false_edge , ratio ) : X_train_true , X_test_true , Y_train_true_edge , Y_test_true_edge = sk_ms . train_test_split ( X_true , Y_true_edge , test_size = 1 - ratio ) X_train_false , X_test_false , Y_train_false_edge , Y_test_false_edge = sk_ms . train_test_split ( X_false , Y_false_edge , test_size = 1 - ratio ) true_edges_test_source = Y_test_true_edge . T [ 2 ] . reshape ( - 1 , 1 ) true_edges_test_target = Y_test_true_edge . T [ 3 ] . reshape ( - 1 , 1 ) X_train , X_test , Y_train_edge , Y_test_edge = np . concatenate ( ( X_train_true , X_train_false ) , axis = 0 ) , np . concatenate ( ( X_test_true , X_test_false ) , axis = 0 ) , np . concatenate ( ( Y_train_true_edge , Y_train_false_edge ) , axis = 0 ) , np . concatenate ( ( Y_test_true_edge , Y_test_false_edge ) , axis = 0 ) Y_train = np . array ( [ Y_train_edge . T [ 0 ] . reshape ( - 1 , 1 ) , Y_train_edge . T [ 1 ] . reshape ( - 1 , 1 ) ] ) . T . reshape ( - 1 , 2 ) . astype ( int ) true_edges_test = np . array ( [ true_edges_test_source , true_edges_test_target ] ) . T [ 0 ] return X_train , Y_train , true_edges_test
short_docstring: split the data into rain and test for the true edges and the false one.
short_docstring_tokens: split the data into rain and test for the true edges and the false one .
comment: """
          split the data into rain and test for the true edges and the false one.
          :param ratio: determine the train size.
          :return: THe split data
          """