xdeepfm.py

# -*- coding:utf-8 -*-
"""
Author:
    Weichen Shen, wcshen1994@163.com

Reference:
    [1] Lian J, Zhou X, Zhang F, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems[J]. arXiv preprint arXiv:1803.05170, 2018.(https://arxiv.org/pdf/1803.05170.pdf)
"""
import tensorflow as tf

from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns
from ..layers.core import PredictionLayer, DNN
from ..layers.interaction import CIN
from ..layers.utils import concat_func, add_func, combined_dnn_input


def xDeepFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 256),
            cin_layer_size=(128, 128,), cin_split_half=True, cin_activation='relu', l2_reg_linear=0.00001,
            l2_reg_embedding=0.00001, l2_reg_dnn=0, l2_reg_cin=0, seed=1024, dnn_dropout=0,
            dnn_activation='relu', dnn_use_bn=False, task='binary'):
    """Instantiates the xDeepFM architecture.

    :param linear_feature_columns: An iterable containing all the features used by linear part of the model.
    :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
    :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net
    :param cin_layer_size: list,list of positive integer or empty list, the feature maps  in each hidden layer of Compressed Interaction Network
    :param cin_split_half: bool.if set to True, half of the feature maps in each hidden will connect to output unit
    :param cin_activation: activation function used on feature maps
    :param l2_reg_linear: float. L2 regularizer strength applied to linear part
    :param l2_reg_embedding: L2 regularizer strength applied to embedding vector
    :param l2_reg_dnn: L2 regularizer strength applied to deep net
    :param l2_reg_cin: L2 regularizer strength applied to CIN.
    :param seed: integer ,to use as random seed.
    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param dnn_activation: Activation function to use in DNN
    :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.
    """

    features = build_input_features(
        linear_feature_columns + dnn_feature_columns)

    inputs_list = list(features.values())

    linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear',
                                    l2_reg=l2_reg_linear)

    sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
                                                                         l2_reg_embedding, seed)

    fm_input = concat_func(sparse_embedding_list, axis=1)

    dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
    dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input)
    dnn_logit = tf.keras.layers.Dense(
        1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(dnn_output)

    final_logit = add_func([linear_logit, dnn_logit])

    if len(cin_layer_size) > 0:
        exFM_out = CIN(cin_layer_size, cin_activation,
                       cin_split_half, l2_reg_cin, seed)(fm_input)
        exFM_logit = tf.keras.layers.Dense(1, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(exFM_out)
        final_logit = add_func([final_logit, exFM_logit])

    output = PredictionLayer(task)(final_logit)

    model = tf.keras.models.Model(inputs=inputs_list, outputs=output)
    return model