def check_type()

in recommenders/models/deeprec/deeprec_utils.py [0:0]
82 lines of code
13 McCabe index (conditional complexity)

def check_type(config):
    """Check that the config parameters are the correct type

    Args:
        config (dict): Configuration dictionary.

    Raises:
        TypeError: If the parameters are not the correct type.
    """

    int_parameters = [
        "word_size",
        "entity_size",
        "doc_size",
        "history_size",
        "FEATURE_COUNT",
        "FIELD_COUNT",
        "dim",
        "epochs",
        "batch_size",
        "show_step",
        "save_epoch",
        "PAIR_NUM",
        "DNN_FIELD_NUM",
        "attention_layer_sizes",
        "n_user",
        "n_item",
        "n_user_attr",
        "n_item_attr",
        "item_embedding_dim",
        "cate_embedding_dim",
        "user_embedding_dim",
        "max_seq_length",
        "hidden_size",
        "T",
        "L",
        "n_v",
        "n_h",
        "kernel_size",
        "min_seq_length",
        "attention_size",
        "epochs",
        "batch_size",
        "show_step",
        "save_epoch",
        "train_num_ngs",
    ]
    for param in int_parameters:
        if param in config and not isinstance(config[param], int):
            raise TypeError("Parameters {0} must be int".format(param))

    float_parameters = [
        "init_value",
        "learning_rate",
        "embed_l2",
        "embed_l1",
        "layer_l2",
        "layer_l1",
        "mu",
    ]
    for param in float_parameters:
        if param in config and not isinstance(config[param], float):
            raise TypeError("Parameters {0} must be float".format(param))

    str_parameters = [
        "train_file",
        "eval_file",
        "test_file",
        "infer_file",
        "method",
        "load_model_name",
        "infer_model_name",
        "loss",
        "optimizer",
        "init_method",
        "attention_activation",
        "user_vocab",
        "item_vocab",
        "cate_vocab",
    ]
    for param in str_parameters:
        if param in config and not isinstance(config[param], str):
            raise TypeError("Parameters {0} must be str".format(param))

    list_parameters = [
        "layer_sizes",
        "activation",
        "dropout",
        "att_fcn_layer_sizes",
        "dilations",
    ]
    for param in list_parameters:
        if param in config and not isinstance(config[param], list):
            raise TypeError("Parameters {0} must be list".format(param))