def check_nn_config()

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

def check_nn_config(f_config):
    """Check neural networks configuration.

    Args:
        f_config (dict): Neural network configuration.

    Raises:
        ValueError: If the parameters are not correct.
    """
    if f_config["model_type"] in ["fm", "FM"]:
        required_parameters = ["FEATURE_COUNT", "dim", "loss", "data_format", "method"]
    elif f_config["model_type"] in ["lr", "LR"]:
        required_parameters = ["FEATURE_COUNT", "loss", "data_format", "method"]
    elif f_config["model_type"] in ["dkn", "DKN"]:
        required_parameters = [
            "doc_size",
            "history_size",
            "wordEmb_file",
            "entityEmb_file",
            "contextEmb_file",
            "news_feature_file",
            "user_history_file",
            "word_size",
            "entity_size",
            "use_entity",
            "use_context",
            "data_format",
            "dim",
            "layer_sizes",
            "activation",
            "attention_activation",
            "attention_activation",
            "attention_dropout",
            "loss",
            "data_format",
            "dropout",
            "method",
            "num_filters",
            "filter_sizes",
        ]
    elif f_config["model_type"] in ["exDeepFM", "xDeepFM"]:
        required_parameters = [
            "FIELD_COUNT",
            "FEATURE_COUNT",
            "method",
            "dim",
            "layer_sizes",
            "cross_layer_sizes",
            "activation",
            "loss",
            "data_format",
            "dropout",
        ]
    if f_config["model_type"] in ["gru4rec", "GRU4REC", "GRU4Rec"]:
        required_parameters = [
            "item_embedding_dim",
            "cate_embedding_dim",
            "max_seq_length",
            "loss",
            "method",
            "user_vocab",
            "item_vocab",
            "cate_vocab",
            "hidden_size",
        ]
    elif f_config["model_type"] in ["caser", "CASER", "Caser"]:
        required_parameters = [
            "item_embedding_dim",
            "cate_embedding_dim",
            "user_embedding_dim",
            "max_seq_length",
            "loss",
            "method",
            "user_vocab",
            "item_vocab",
            "cate_vocab",
            "T",
            "L",
            "n_v",
            "n_h",
            "min_seq_length",
        ]
    elif f_config["model_type"] in ["asvd", "ASVD", "a2svd", "A2SVD"]:
        required_parameters = [
            "item_embedding_dim",
            "cate_embedding_dim",
            "max_seq_length",
            "loss",
            "method",
            "user_vocab",
            "item_vocab",
            "cate_vocab",
        ]
    elif f_config["model_type"] in ["slirec", "sli_rec", "SLI_REC", "Sli_rec"]:
        required_parameters = [
            "item_embedding_dim",
            "cate_embedding_dim",
            "max_seq_length",
            "loss",
            "method",
            "user_vocab",
            "item_vocab",
            "cate_vocab",
            "attention_size",
            "hidden_size",
            "att_fcn_layer_sizes",
        ]
    elif f_config["model_type"] in [
        "nextitnet",
        "next_it_net",
        "NextItNet",
        "NEXT_IT_NET",
    ]:
        required_parameters = [
            "item_embedding_dim",
            "cate_embedding_dim",
            "user_embedding_dim",
            "max_seq_length",
            "loss",
            "method",
            "user_vocab",
            "item_vocab",
            "cate_vocab",
            "dilations",
            "kernel_size",
            "min_seq_length",
        ]
    else:
        required_parameters = []

    # check required parameters
    for param in required_parameters:
        if param not in f_config:
            raise ValueError("Parameters {0} must be set".format(param))

    if f_config["model_type"] in ["exDeepFM", "xDeepFM"]:
        if f_config["data_format"] != "ffm":
            raise ValueError(
                "For xDeepFM model, data format must be 'ffm', but your set is {0}".format(
                    f_config["data_format"]
                )
            )
    elif f_config["model_type"] in ["dkn", "DKN"]:
        if f_config["data_format"] != "dkn":
            raise ValueError(
                "For dkn model, data format must be 'dkn', but your set is {0}".format(
                    f_config["data_format"]
                )
            )
    check_type(f_config)