# -*- coding:utf-8 -*-
"""
Author:
Harshit Pande
Reference:
[1] Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
(https://arxiv.org/pdf/1806.03514.pdf)
"""
from itertools import chain
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense
from ..feature_column import build_input_features, get_linear_logit, DEFAULT_GROUP_NAME, input_from_feature_columns
from ..layers.core import PredictionLayer, DNN
from ..layers.interaction import FwFMLayer
from ..layers.utils import concat_func, add_func, combined_dnn_input
[docs]def FwFM(linear_feature_columns, dnn_feature_columns, fm_group=(DEFAULT_GROUP_NAME,), dnn_hidden_units=(256, 128, 64),
l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_field_strength=0.00001, l2_reg_dnn=0,
seed=1024, dnn_dropout=0, dnn_activation='relu', dnn_use_bn=False, task='binary'):
"""Instantiates the FwFM Network 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 fm_group: list, group_name of features that will be used to do feature interactions.
:param dnn_hidden_units: list,list of positive integer or empty list if do not want DNN, 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_field_strength: float. L2 regularizer strength applied to the field pair strength parameters
: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 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)
group_embedding_dict, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding, seed,
support_group=True)
fwfm_logit = add_func([FwFMLayer(num_fields=len(v), regularizer=l2_reg_field_strength)
(concat_func(v, axis=1)) for k, v in group_embedding_dict.items() if k in fm_group])
final_logit_components = [linear_logit, fwfm_logit]
if dnn_hidden_units:
dnn_input = combined_dnn_input(list(chain.from_iterable(
group_embedding_dict.values())), 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 = Dense(1, use_bias=False)(dnn_output)
final_logit_components.append(dnn_logit)
final_logit = add_func(final_logit_components)
output = PredictionLayer(task)(final_logit)
model = Model(inputs=inputs_list, outputs=output)
return model