# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen, weichenswc@163.com
Reference:
[1] Gai K, Zhu X, Li H, et al. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction[J]. arXiv preprint arXiv:1704.05194, 2017.(https://arxiv.org/abs/1704.05194)
"""
from tensorflow.python.keras.layers import Activation, dot
from tensorflow.python.keras.models import Model
from ..feature_column import build_input_features, get_linear_logit
from ..layers.core import PredictionLayer
from ..layers.utils import concat_func
[docs]def MLR(region_feature_columns, base_feature_columns=None, region_num=4,
l2_reg_linear=1e-5, seed=1024, task='binary',
bias_feature_columns=None):
"""Instantiates the Mixed Logistic Regression/Piece-wise Linear Model.
:param region_feature_columns: An iterable containing all the features used by region part of the model.
:param base_feature_columns: An iterable containing all the features used by base part of the model.
:param region_num: integer > 1,indicate the piece number
:param l2_reg_linear: float. L2 regularizer strength applied to weight
:param seed: integer ,to use as random seed.
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:param bias_feature_columns: An iterable containing all the features used by bias part of the model.
:return: A Keras model instance.
"""
if region_num <= 1:
raise ValueError("region_num must > 1")
if base_feature_columns is None or len(base_feature_columns) == 0:
base_feature_columns = region_feature_columns
if bias_feature_columns is None:
bias_feature_columns = []
features = build_input_features(region_feature_columns + base_feature_columns + bias_feature_columns)
inputs_list = list(features.values())
region_score = get_region_score(features, region_feature_columns, region_num, l2_reg_linear, seed)
learner_score = get_learner_score(features, base_feature_columns, region_num, l2_reg_linear, seed, task=task)
final_logit = dot([region_score, learner_score], axes=-1)
if bias_feature_columns is not None and len(bias_feature_columns) > 0:
bias_score = get_learner_score(features, bias_feature_columns, 1, l2_reg_linear, seed, prefix='bias_',
task='binary')
final_logit = dot([final_logit, bias_score], axes=-1)
model = Model(inputs=inputs_list, outputs=final_logit)
return model
def get_region_score(features, feature_columns, region_number, l2_reg, seed, prefix='region_', seq_mask_zero=True):
region_logit = concat_func([get_linear_logit(features, feature_columns, seed=seed + i,
prefix=prefix + str(i + 1), l2_reg=l2_reg) for i in
range(region_number)])
return Activation('softmax')(region_logit)
def get_learner_score(features, feature_columns, region_number, l2_reg, seed, prefix='learner_', seq_mask_zero=True,
task='binary'):
region_score = [PredictionLayer(task=task, use_bias=False)(
get_linear_logit(features, feature_columns, seed=seed + i, prefix=prefix + str(i + 1),
l2_reg=l2_reg)) for i in
range(region_number)]
return concat_func(region_score)