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GAMMA.py
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import tensorflow as tf
import numpy as np
import pdb,sys
sys.path.append('./.')
sys.path.append('./utils/.')
sys.path.append('./utils')
#############################################################
# The following packages -- models and utils are #
# adapted from https://github.com/PetarV-/GAT/ #
#############################################################
from models import SpGAT
from utils import process
from MultiHeadMemoryNetwork import MultiHeadMemoryNetwork
from Attention import Attention
class GAMMA(object):
def __init__(self,params):
self.num_factors = params.num_factors
self.num_users = params.num_users
self.num_items = params.num_items
self.num_doms = params.num_doms
self.attr_dim = params.attn_head_size
self.reg_lam = params.reg_lam
self.reg_w = params.reg_w
self.reg_b = params.reg_b
self.initializer = params.initializer
self.num_views = params.num_views
self.num_attn_heads = params.num_attn_heads
self.num_memory_heads = params.num_memory_heads
self.attn_head_size = params.attn_head_size
self.memory_head_size = params.memory_head_size
self.proj_keep = params.proj_keep
self.attention_keep = params.attention_keep
self.params = params
# list initializtion ====================================
self.memnet = [None] * self.num_views
self.mult_views = [None] * self.num_views
self.item_view_embeds = [None] * self.num_views
self.item_attr_mat = [None] * self.num_views
self.user_embeds_view = [None] * self.num_views
self.proj_item_view = [None] * self.num_views
self.mhead_item_view_output = [None] * self.num_views
self.m3_item_view_output = [None] * self.num_views
# gat ====
self.model = [None] * self.num_views
self.biases = [None] * self.num_views
self.num_nodes = [None] * self.num_views
self.X_features_view = [None] * self.num_views
self.bias_in = [None] * self.num_views
self.logits = [None] * self.num_views
self.item_view_embeddings = [None] * self.num_views
self.item_view_embeds = [None] * self.num_views
self.X_features_item_entity_view = [None] * self.num_views
# =====================================================
self.adjacency_view_matrix = params.adjacency_view_matrix
for view in range(self.num_views):
self.model[view] = SpGAT()
self.biases[view] = process.preprocess_adj_bias(params.adjacency_view_matrix[view])
self.num_nodes[view] = params.adjacency_view_matrix[view].shape[0]
self.out_size = params.num_factors
self.hid_units = params.hid_units
self.n_heads = params.n_heads
self.attn_keep = params.attn_keep
self.ffd_keep = params.ffd_keep
self.proj_keep = params.proj_keep
self.residual = False
self.nonlinearity = tf.nn.elu
self.dense = tf.keras.layers.Dense(self.attn_head_size,use_bias=True,activation='elu')
self.dense_attn = tf.keras.layers.Dense(self.attn_head_size/2,use_bias=True,activation='elu')
self.dense_w = tf.keras.layers.Dense(1,use_bias=False,activation='sigmoid')
def define_model(self,user_indices,item_indices,dom_indices,true_rating,keep_prob,valid_clip):
self.user_indices = user_indices
self.item_indices = item_indices
self.dom_indices = dom_indices
self.true_rating = true_rating
self.keep_prob = keep_prob
self.valid_clip = valid_clip
self.is_training = tf.equal(0.0,valid_clip)
# variables
self.user_embeddings = tf.Variable(self.initializer(shape=[self.num_users,self.num_factors]),dtype=tf.float32,name='user_embedding')
self.item_embeddings = tf.Variable(self.initializer(shape=[self.num_items,self.num_factors]),dtype=tf.float32,name='item_embedding')
# definitions
self.user_embeds = tf.nn.embedding_lookup(self.user_embeddings, self.user_indices)
self.item_embeds = tf.nn.embedding_lookup(self.item_embeddings, self.item_indices)
self.multiplied_output1 = tf.multiply(self.user_embeds,self.item_embeds)
self.multiplied_output1 = tf.nn.dropout(self.multiplied_output1, self.keep_prob)
for view in range(self.num_views):
# gat layer ============================
self.X_features_item_entity_view[view] = tf.Variable(self.initializer(shape=[self.num_nodes[view],self.num_factors]),trainable=True,dtype=tf.float32,name='X_features_item_entity')
self.X_features_view[view] = tf.expand_dims(self.X_features_item_entity_view[view],0)
self.bias_in[view] = tf.SparseTensor(indices=self.biases[view][0],values=self.biases[view][1],dense_shape=self.biases[view][2])
self.logits[view] = self.model[view].inference(self.X_features_view[view], self.out_size, nb_nodes=self.num_nodes[view], training=self.is_training,
attn_drop = (1-self.attn_keep) * (1-self.valid_clip), ffd_drop = (1-self.ffd_keep) * (1-self.valid_clip),
bias_mat=self.bias_in[view],
hid_units=self.hid_units, n_heads=self.n_heads,
residual=self.residual, activation=self.nonlinearity)
self.item_view_embeddings[view] = tf.reshape(self.logits[view], [-1, self.n_heads[-1] * self.out_size])
self.item_view_embeds[view] = tf.nn.embedding_lookup(self.item_view_embeddings[view], self.item_indices)
self.proj_item_view[view] = self.dense(self.item_view_embeds[view])
self.proj_item_view[view] = tf.nn.dropout(self.proj_item_view[view], self.keep_prob)
# m3 layer ========================
self.memnet[view] = MultiHeadMemoryNetwork(num_users=self.num_users,attr_dim=self.attr_dim,initializer=self.initializer,
num_memory_heads=self.num_memory_heads,num_attn_heads=self.num_attn_heads,
attn_head_size=self.attn_head_size,memory_head_size=self.memory_head_size)
self.item_indices_expanded = tf.expand_dims(item_indices,1)
self.m3_item_view_output[view] = self.memnet[view].call(user_embeds=self.user_embeds,user_indices=self.user_indices,
item_view_embeds=tf.expand_dims(self.proj_item_view[view],1),
attention_keep=self.valid_clip + self.attention_keep * (1-self.valid_clip))
# Attention ==================
self.user_embeddings_attn = tf.Variable(self.initializer(shape=[self.num_users,self.attn_head_size]),dtype=tf.float32,name='user_embedding2')
self.user_embeds_attn = tf.nn.embedding_lookup(self.user_embeddings_attn, self.user_indices)
self.attn = Attention(num_factors=self.num_factors,attr_dim=self.attn_head_size,seq_len=self.num_views,initializer=self.initializer)
#self.attn_weights = self.attn.get_attention_weights(self.user_embeds_attn,tf.concat(self.m3_item_view_output,axis=1),atten_type='bahdanau')
self.attn_weights = self.attn.get_attention_weights(self.user_embeds_attn,tf.concat(self.m3_item_view_output,axis=1),atten_type='luong')
self.attn_weights = tf.nn.dropout(self.attn_weights, self.valid_clip + self.attention_keep * (1-self.valid_clip))
self.cont_mat = self.attn.get_context_matrix(self.attn_weights,tf.concat(self.m3_item_view_output,axis=1))
self.attn_mat = self.attn.get_attention_matrix(self.cont_mat,self.user_embeds_attn,inter_type='context')
self.final_item_view_output = self.attn_mat
self.final_item_view_output = self.dense_attn(self.final_item_view_output)
# ============================
self.user_embeddings_attr = tf.Variable(self.initializer(shape=[self.num_users,self.attn_head_size/2]),dtype=tf.float32,name='user_embedding2')
self.user_embeds_attr = tf.nn.embedding_lookup(self.user_embeddings_attr, self.user_indices)
self.multiplied_output2 = tf.multiply(self.user_embeds_attr,tf.reshape(self.final_item_view_output,shape=[-1,self.attn_head_size/2])) ##
self.multiplied_output2 = tf.nn.dropout(self.multiplied_output2, self.valid_clip + self.proj_keep * (1-self.valid_clip))
# ============================
self.mult_cat = tf.concat([self.multiplied_output1,self.multiplied_output2],axis=1) ##2
self.pred_rating = tf.reshape(self.dense_w(self.mult_cat),shape=[-1])
def define_loss(self,loss_type='all'):
self.regularization_loss = tf.constant(0.0)