from __future__ import print_function
from __future__ import absolute_import
import os
import sys
import tensorflow as tf
from ultra.ranking_model import BaseRankingModel
import ultra
class Linear(BaseRankingModel):
"""A linear model for learning to rank.
This class implements a linear ranking model. It's essientially a logistic regression model.
"""
def __init__(self, hparams_str):
"""Create the network.
Args:
hparams_str: (String) The hyper-parameters used to build the network.
"""
self.hparams = ultra.utils.hparams.HParams(
initializer='None', # Set parameter initializer
norm="layer" # Set the default normalization
)
self.hparams.parse(hparams_str)
self.initializer = None
self.layer_norm = None
if self.hparams.initializer in BaseRankingModel.INITIALIZER_DIC:
self.initializer = BaseRankingModel.INITIALIZER_DIC[self.hparams.initializer]
self.model_parameters = {}
def build(self, input_list, noisy_params=None,
noise_rate=0.05, is_training=False, **kwargs):
""" Create the Linear model
Args:
input_list: (list<tf.tensor>) A list of tensors containing the features
for a list of documents.
noisy_params: (dict<parameter_name, tf.variable>) A dictionary of noisy parameters to add.
noise_rate: (float) A value specify how much noise to add.
is_training: (bool) A flag indicating whether the model is running in training mode.
Returns:
A list of tf.Tensor containing the ranking scores for each instance in input_list.
"""
with tf.variable_scope(tf.get_variable_scope(), initializer=self.initializer,
reuse=tf.AUTO_REUSE):
input_data = tf.concat(input_list, axis=0)
output_data = input_data
output_sizes = [1]
if self.layer_norm is None and self.hparams.norm in BaseRankingModel.NORM_FUNC_DIC:
self.layer_norm = []
for j in range(len(output_sizes)):
self.layer_norm.append(BaseRankingModel.NORM_FUNC_DIC[self.hparams.norm](
name="layer_norm_%d" % j))
current_size = output_data.get_shape()[-1].value
for j in range(len(output_sizes)):
if self.layer_norm is not None:
output_data = self.layer_norm[j](
output_data, training=is_training)
expand_W = self.get_variable(
"linear_W_%d" % j, [current_size, output_sizes[j]], noisy_params=noisy_params, noise_rate=noise_rate)
expand_b = self.get_variable(
"linear_b_%d" % j, [output_sizes[j]], noisy_params=noisy_params, noise_rate=noise_rate)
output_data = tf.nn.bias_add(
tf.matmul(output_data, expand_W), expand_b)
return tf.split(output_data, len(input_list), axis=0)
