# -*- coding:utf-8 -*-
"""
Author:
Harshit Pande
Reference:
[1] Field-Embedded Factorization Machines for Click-through Rate Prediction]
(https://arxiv.org/pdf/2009.09931.pdf)
this file also supports all the possible Ablation studies for reproducibility
"""
from itertools import chain
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Lambda
from ..feature_column import input_from_feature_columns, get_linear_logit, build_input_features, DEFAULT_GROUP_NAME
from ..layers.core import PredictionLayer, DNN
from ..layers.interaction import FEFMLayer
from ..layers.utils import concat_func, combined_dnn_input, reduce_sum, add_func
[docs]def DeepFEFM(linear_feature_columns, dnn_feature_columns, use_fefm=True,
dnn_hidden_units=(256, 128, 64), l2_reg_linear=0.00001, l2_reg_embedding_feat=0.00001,
l2_reg_embedding_field=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0.0,
exclude_feature_embed_in_dnn=False,
use_linear=True, use_fefm_embed_in_dnn=True, dnn_activation='relu', dnn_use_bn=False, task='binary'):
"""Instantiates the DeepFEFM Network architecture or the shallow FEFM architecture (Ablation studies supported)
: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 fm_group: list, group_name of features that will be used to do feature interactions.
:param use_fefm: bool,use FEFM logit or not (doesn't effect FEFM embeddings in DNN, controls only the use of final FEFM logit)
:param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN
:param l2_reg_linear: float. L2 regularizer strength applied to linear part
:param l2_reg_embedding_feat: float. L2 regularizer strength applied to embedding vector of features
:param l2_reg_embedding_field: float, L2 regularizer to field embeddings
: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 exclude_feature_embed_in_dnn: bool, used in ablation studies for removing feature embeddings in DNN
:param use_linear: bool, used in ablation studies
:param use_fefm_embed_in_dnn: bool, True if FEFM interaction embeddings are to be used in FEFM (set False for Ablation)
: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, l2_reg=l2_reg_linear, seed=seed, prefix='linear')
group_embedding_dict, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding_feat,
seed, support_group=True)
fefm_interaction_embedding = concat_func([FEFMLayer(
regularizer=l2_reg_embedding_field)(concat_func(v, axis=1))
for k, v in group_embedding_dict.items() if k in [DEFAULT_GROUP_NAME]],
axis=1)
dnn_input = combined_dnn_input(list(chain.from_iterable(group_embedding_dict.values())), dense_value_list)
# if use_fefm_embed_in_dnn is set to False it is Ablation4 (Use false only for Ablation)
if use_fefm_embed_in_dnn:
if exclude_feature_embed_in_dnn:
# Ablation3: remove feature vector embeddings from the DNN input
dnn_input = fefm_interaction_embedding
else:
# No ablation
dnn_input = concat_func([dnn_input, fefm_interaction_embedding], axis=1)
dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input)
dnn_logit = Dense(1, use_bias=False, )(dnn_out)
fefm_logit = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=True))(fefm_interaction_embedding)
if len(dnn_hidden_units) == 0 and use_fefm is False and use_linear is True: # only linear
final_logit = linear_logit
elif len(dnn_hidden_units) == 0 and use_fefm is True and use_linear is True: # linear + FEFM
final_logit = add_func([linear_logit, fefm_logit])
elif len(dnn_hidden_units) > 0 and use_fefm is False and use_linear is True: # linear +　Deep # Ablation1
final_logit = add_func([linear_logit, dnn_logit])
elif len(dnn_hidden_units) > 0 and use_fefm is True and use_linear is True: # linear + FEFM + Deep
final_logit = add_func([linear_logit, fefm_logit, dnn_logit])
elif len(dnn_hidden_units) == 0 and use_fefm is True and use_linear is False: # only FEFM (shallow)
final_logit = fefm_logit
elif len(dnn_hidden_units) > 0 and use_fefm is False and use_linear is False: # only Deep
final_logit = dnn_logit
elif len(dnn_hidden_units) > 0 and use_fefm is True and use_linear is False: # FEFM + Deep # Ablation2
final_logit = add_func([fefm_logit, dnn_logit])
else:
raise NotImplementedError
output = PredictionLayer(task)(final_logit)
model = Model(inputs=inputs_list, outputs=output)
return model