<filename>deepctr/models/pnn_udg.py
# -*- coding:utf-8 -*-
"""
Author:
    <NAME>,<EMAIL>

Reference:
    [1] <NAME>, <NAME>, <NAME>, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(https://arxiv.org/pdf/1611.00144.pdf)
"""

import tensorflow as tf

from collections import OrderedDict
from ..feature_column import build_input_features, input_from_feature_columns
from ..layers.core import PredictionLayer, DNN_UDG
from ..layers.interaction import InnerProductLayer, OutterProductLayer
from ..layers.utils import concat_func, combined_dnn_input

from collections import OrderedDict
from ..feature_column import build_input_features, get_linear_logit, DEFAULT_GROUP_NAME, input_from_feature_columns, SparseFeat
from ..layers.core import PredictionLayer, DNN_UDG
from ..layers.interaction import FM
from ..inputs import create_embedding_dict, embedding_lookup
from ..layers.utils import concat_func, add_func, combined_dnn_input


def PNN_UDG(dnn_feature_columns, untrain_feature_columns, dnn_hidden_units=(128, 128), l2_reg_embedding=1e-5, l2_reg_dnn=0,
        seed=1024, dnn_dropout=0, dnn_activation='relu', use_inner=True, use_outter=False, kernel_type='mat',
        task='binary', uid_feature_name='', udg_embedding_size=128):
    """Instantiates the Product-based Neural Network architecture.

    :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 l2_reg_embedding: float . L2 regularizer strength applied to embedding vector
    :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
    :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 use_inner: bool,whether use inner-product or not.
    :param use_outter: bool,whether use outter-product or not.
    :param kernel_type: str,kernel_type used in outter-product,can be ``'mat'`` , ``'vec'`` or ``'num'``
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.
    """

    if kernel_type not in ['mat', 'vec', 'num']:
        raise ValueError("kernel_type must be mat,vec or num")

    features = build_input_features(dnn_feature_columns)
    uid_features = OrderedDict()
    uid_features[uid_feature_name] = features[uid_feature_name]
    uid_feature_columns = [x for x in dnn_feature_columns if x.name == uid_feature_name]

    inputs_list = list(features.values())

    sparse_embedding_list, dense_value_list, untrain_embedding_dict = input_from_feature_columns(features, dnn_feature_columns,untrain_feature_columns, l2_reg_embedding, seed)
    inner_product = tf.keras.layers.Flatten()(
        InnerProductLayer()(sparse_embedding_list))
    outter_product = OutterProductLayer(kernel_type)(sparse_embedding_list)

    # ipnn deep input
    linear_signal = tf.keras.layers.Reshape(
        [sum(map(lambda x: int(x.shape[-1]), sparse_embedding_list))])(concat_func(sparse_embedding_list))

    if use_inner and use_outter:
        deep_input = tf.keras.layers.Concatenate()(
            [linear_signal, inner_product, outter_product])
    elif use_inner:
        deep_input = tf.keras.layers.Concatenate()(
            [linear_signal, inner_product])
    elif use_outter:
        deep_input = tf.keras.layers.Concatenate()(
            [linear_signal, outter_product])
    else:
        deep_input = linear_signal
    
    uid_embedding_dict = create_embedding_dict(uid_feature_columns, [], 0.00001, prefix='udg', 
                                           seq_mask_zero=True)
    uid_emb_list = embedding_lookup(uid_embedding_dict, uid_features, uid_feature_columns, [], 
                                       return_feat_list=[], to_list=True)
    uid_emb_list = uid_emb_list + untrain_embedding_dict

    dnn_input = combined_dnn_input([deep_input], dense_value_list, udg_label=1, 
                                   udg_embedding_list=uid_emb_list)
    print(dnn_input)
    udg_embedding_size = (len(untrain_feature_columns)+1)*udg_embedding_size
    dnn_out = DNN_UDG(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, False, seed, udg_embedding_size=udg_embedding_size)(dnn_input)
    dnn_logit = tf.keras.layers.Dense(1, use_bias=False, activation=None)(dnn_out)

    output = PredictionLayer(task)(dnn_logit)

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

<filename>deepctr/models/pnn_udg.py
# -*- coding:utf-8 -*-
"""
Author:
    <NAME>,<EMAIL>

Reference:
    [1] <NAME>, <NAME>, <NAME>, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(https://arxiv.org/pdf/1611.00144.pdf)
"""

import tensorflow as tf

from collections import OrderedDict
from ..feature_column import build_input_features, input_from_feature_columns
from ..layers.core import PredictionLayer, DNN_UDG
from ..layers.interaction import InnerProductLayer, OutterProductLayer
from ..layers.utils import concat_func, combined_dnn_input

from collections import OrderedDict
from ..feature_column import build_input_features, get_linear_logit, DEFAULT_GROUP_NAME, input_from_feature_columns, SparseFeat
from ..layers.core import PredictionLayer, DNN_UDG
from ..layers.interaction import FM
from ..inputs import create_embedding_dict, embedding_lookup
from ..layers.utils import concat_func, add_func, combined_dnn_input


def PNN_UDG(dnn_feature_columns, untrain_feature_columns, dnn_hidden_units=(128, 128), l2_reg_embedding=1e-5, l2_reg_dnn=0,
        seed=1024, dnn_dropout=0, dnn_activation='relu', use_inner=True, use_outter=False, kernel_type='mat',
        task='binary', uid_feature_name='', udg_embedding_size=128):
    """Instantiates the Product-based Neural Network architecture.

    :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 l2_reg_embedding: float . L2 regularizer strength applied to embedding vector
    :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
    :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 use_inner: bool,whether use inner-product or not.
    :param use_outter: bool,whether use outter-product or not.
    :param kernel_type: str,kernel_type used in outter-product,can be ``'mat'`` , ``'vec'`` or ``'num'``
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.
    """

    if kernel_type not in ['mat', 'vec', 'num']:
        raise ValueError("kernel_type must be mat,vec or num")

    features = build_input_features(dnn_feature_columns)
    uid_features = OrderedDict()
    uid_features[uid_feature_name] = features[uid_feature_name]
    uid_feature_columns = [x for x in dnn_feature_columns if x.name == uid_feature_name]

    inputs_list = list(features.values())

    sparse_embedding_list, dense_value_list, untrain_embedding_dict = input_from_feature_columns(features, dnn_feature_columns,untrain_feature_columns, l2_reg_embedding, seed)
    inner_product = tf.keras.layers.Flatten()(
        InnerProductLayer()(sparse_embedding_list))
    outter_product = OutterProductLayer(kernel_type)(sparse_embedding_list)

    # ipnn deep input
    linear_signal = tf.keras.layers.Reshape(
        [sum(map(lambda x: int(x.shape[-1]), sparse_embedding_list))])(concat_func(sparse_embedding_list))

    if use_inner and use_outter:
        deep_input = tf.keras.layers.Concatenate()(
            [linear_signal, inner_product, outter_product])
    elif use_inner:
        deep_input = tf.keras.layers.Concatenate()(
            [linear_signal, inner_product])
    elif use_outter:
        deep_input = tf.keras.layers.Concatenate()(
            [linear_signal, outter_product])
    else:
        deep_input = linear_signal
    
    uid_embedding_dict = create_embedding_dict(uid_feature_columns, [], 0.00001, prefix='udg', 
                                           seq_mask_zero=True)
    uid_emb_list = embedding_lookup(uid_embedding_dict, uid_features, uid_feature_columns, [], 
                                       return_feat_list=[], to_list=True)
    uid_emb_list = uid_emb_list + untrain_embedding_dict

    dnn_input = combined_dnn_input([deep_input], dense_value_list, udg_label=1, 
                                   udg_embedding_list=uid_emb_list)
    print(dnn_input)
    udg_embedding_size = (len(untrain_feature_columns)+1)*udg_embedding_size
    dnn_out = DNN_UDG(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, False, seed, udg_embedding_size=udg_embedding_size)(dnn_input)
    dnn_logit = tf.keras.layers.Dense(1, use_bias=False, activation=None)(dnn_out)

    output = PredictionLayer(task)(dnn_logit)

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

# -*- coding:utf-8 -*- Author:     <NAME>,<EMAIL>  Reference:     [1] <NAME>, <NAME>, <NAME>, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(https://arxiv.org/pdf/1611.00144.pdf) Instantiates the Product-based Neural Network architecture.      :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 l2_reg_embedding: float . L2 regularizer strength applied to embedding vector     :param l2_reg_dnn: float. L2 regularizer strength applied to DNN     :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 use_inner: bool,whether use inner-product or not.     :param use_outter: bool,whether use outter-product or not.     :param kernel_type: str,kernel_type used in outter-product,can be ``'mat'`` , ``'vec'`` or ``'num'``     :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss     :return: A Keras model instance. # ipnn deep input