Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add HRDR model #518

Merged
merged 3 commits into from
Jul 21, 2023
Merged

Add HRDR model #518

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
0b0ee6f
Add HRDR model
lthoang Jun 28, 2023
40d21a9
refactor code
lthoang Jun 29, 2023
4433a6b
refactor code to use save and load function
lthoang Jul 13, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
256 changes: 149 additions & 107 deletions cornac/models/hrdr/hrdr.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,14 +1,15 @@
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, initializers, Input
from tensorflow.keras import layers, initializers
from tensorflow.python.keras.preprocessing.sequence import pad_sequences

from ...utils import get_rng
from ...utils.init_utils import uniform
from ..narre.narre import TextProcessor, AddGlobalBias

def get_data(batch_ids, train_set, max_text_length, by="user", max_num_review=None):

def get_data(batch_ids, train_set, max_text_length, by="user", max_num_review=32):
batch_reviews, batch_num_reviews = [], []
review_group = (
train_set.review_text.user_review
Expand All @@ -26,7 +27,7 @@ def get_data(batch_ids, train_set, max_text_length, by="user", max_num_review=No
)
batch_reviews.append(reviews)
batch_num_reviews.append(len(reviews))
batch_reviews = pad_sequences(batch_reviews, padding="post")
batch_reviews = pad_sequences(batch_reviews, maxlen=max_num_review, padding="post")
batch_num_reviews = np.array(batch_num_reviews).astype(np.int32)
batch_ratings = (
np.zeros((len(batch_ids), train_set.num_items), dtype=np.float32)
Expand All @@ -40,12 +41,109 @@ def get_data(batch_ids, train_set, max_text_length, by="user", max_num_review=No
batch_ratings[batch_inc, jdx] = rating
return batch_reviews, batch_num_reviews, batch_ratings

class Model:
class Model(keras.Model):
def __init__(self, n_users, n_items, n_vocab, global_mean, embedding_matrix,
n_factors=32, embedding_size=100, id_embedding_size=32,
attention_size=16, kernel_sizes=[3], n_filters=64,
n_user_mlp_factors=128, n_item_mlp_factors=128,
dropout_rate=0.5, max_text_length=50):
super().__init__()
self.l_user_review_embedding = layers.Embedding(n_vocab, embedding_size, embeddings_initializer=embedding_matrix, mask_zero=True, name="user_review_embedding")
self.l_item_review_embedding = layers.Embedding(n_vocab, embedding_size, embeddings_initializer=embedding_matrix, mask_zero=True, name="item_review_embedding")
self.l_user_embedding = layers.Embedding(n_users, id_embedding_size, embeddings_initializer="uniform", name="user_embedding")
self.l_item_embedding = layers.Embedding(n_items, id_embedding_size, embeddings_initializer="uniform", name="item_embedding")
self.user_bias = layers.Embedding(n_users, 1, embeddings_initializer=tf.initializers.Constant(0.1), name="user_bias")
self.item_bias = layers.Embedding(n_items, 1, embeddings_initializer=tf.initializers.Constant(0.1), name="item_bias")
self.user_text_processor = TextProcessor(max_text_length, filters=n_filters, kernel_sizes=kernel_sizes, dropout_rate=dropout_rate, name='user_text_processor')
self.item_text_processor = TextProcessor(max_text_length, filters=n_filters, kernel_sizes=kernel_sizes, dropout_rate=dropout_rate, name='item_text_processor')

self.l_user_mlp = keras.models.Sequential([
layers.Dense(n_user_mlp_factors, input_dim=n_items, activation="relu"),
layers.Dense(n_user_mlp_factors // 2, activation="relu"),
layers.Dense(n_filters, activation="relu"),
layers.BatchNormalization(),
])
self.l_item_mlp = keras.models.Sequential([
layers.Dense(n_item_mlp_factors, input_dim=n_users, activation="relu"),
layers.Dense(n_item_mlp_factors // 2, activation="relu"),
layers.Dense(n_filters, activation="relu"),
layers.BatchNormalization(),
])
self.a_user = keras.models.Sequential([
layers.Dense(attention_size, activation="relu", use_bias=True),
layers.Dense(1, activation=None, use_bias=True)
])
self.user_attention = layers.Softmax(axis=1, name="user_attention")
self.a_item = keras.models.Sequential([
layers.Dense(attention_size, activation="relu", use_bias=True),
layers.Dense(1, activation=None, use_bias=True)
])
self.item_attention = layers.Softmax(axis=1, name="item_attention")
self.ou_dropout = layers.Dropout(rate=dropout_rate)
self.oi_dropout = layers.Dropout(rate=dropout_rate)
self.ou = layers.Dense(n_factors, use_bias=True, name="ou")
self.oi = layers.Dense(n_factors, use_bias=True, name="oi")
self.W1 = layers.Dense(1, activation=None, use_bias=False, name="W1")
self.add_global_bias = AddGlobalBias(init_value=global_mean, name="global_bias")

def call(self, inputs, training=False):
i_user_id, i_item_id, i_user_rating, i_user_review, i_user_num_reviews, i_item_rating, i_item_review, i_item_num_reviews = inputs
user_review_h = self.user_text_processor(self.l_user_review_embedding(i_user_review), training=training)
item_review_h = self.item_text_processor(self.l_item_review_embedding(i_item_review), training=training)
user_rating_h = self.l_user_mlp(i_user_rating)
item_rating_h = self.l_item_mlp(i_item_rating)
a_user = self.a_user(
tf.multiply(
user_review_h,
tf.expand_dims(user_rating_h, 1)
)
)
a_user_masking = tf.expand_dims(tf.sequence_mask(tf.reshape(i_user_num_reviews, [-1]), maxlen=i_user_review.shape[1]), -1)
user_attention = self.user_attention(a_user, a_user_masking)
a_item = self.a_item(
tf.multiply(
item_review_h,
tf.expand_dims(item_rating_h, 1)
)
)
a_item_masking = tf.expand_dims(tf.sequence_mask(tf.reshape(i_item_num_reviews, [-1]), maxlen=i_item_review.shape[1]), -1)
item_attention = self.item_attention(a_item, a_item_masking)
ou = tf.multiply(user_attention, user_review_h)
ou = tf.reduce_sum(ou, 1)
if training:
ou = self.ou_dropout(ou, training=training)
ou = self.ou(ou)
oi = tf.multiply(item_attention, item_review_h)
oi = tf.reduce_sum(oi, 1)
if training:
oi = self.oi_dropout(oi, training=training)
oi = self.oi(oi)
pu = tf.concat([
user_rating_h,
ou,
self.l_user_embedding(i_user_id)
], axis=-1)
qi = tf.concat([
item_rating_h,
oi,
self.l_item_embedding(i_item_id)
], axis=-1)
h0 = tf.multiply(pu, qi)
r = self.add_global_bias(
tf.add_n([
self.W1(h0),
self.user_bias(i_user_id),
self.item_bias(i_item_id)
])
)
return r

class HRDRModel:
def __init__(self, n_users, n_items, vocab, global_mean,
n_factors=32, embedding_size=100, id_embedding_size=32,
attention_size=16, kernel_sizes=[3], n_filters=64,
n_user_mlp_factors=128, n_item_mlp_factors=128,
dropout_rate=0.5, max_text_length=50,
dropout_rate=0.5, max_text_length=50, max_num_review=32,
pretrained_word_embeddings=None, verbose=False, seed=None):
self.n_users = n_users
self.n_items = n_items
Expand All @@ -61,6 +159,7 @@ def __init__(self, n_users, n_items, vocab, global_mean,
self.n_item_mlp_factors = n_item_mlp_factors
self.dropout_rate = dropout_rate
self.max_text_length = max_text_length
self.max_num_review = max_num_review
self.verbose = verbose
if seed is not None:
self.rng = get_rng(seed)
Expand All @@ -80,118 +179,61 @@ def __init__(self, n_users, n_items, vocab, global_mean,
print("Number of OOV words: %d" % oov_count)

embedding_matrix = initializers.Constant(embedding_matrix)
i_user_id = Input(shape=(1,), dtype="int32", name="input_user_id")
i_item_id = Input(shape=(1,), dtype="int32", name="input_item_id")
i_user_rating = Input(shape=(self.n_items), dtype="float32", name="input_user_rating")
i_item_rating = Input(shape=(self.n_users), dtype="float32", name="input_item_rating")
i_user_review = Input(shape=(None, self.max_text_length), dtype="int32", name="input_user_review")
i_item_review = Input(shape=(None, self.max_text_length), dtype="int32", name="input_item_review")
i_user_num_reviews = Input(shape=(1,), dtype="int32", name="input_user_number_of_review")
i_item_num_reviews = Input(shape=(1,), dtype="int32", name="input_item_number_of_review")

l_user_review_embedding = layers.Embedding(self.n_vocab, self.embedding_size, embeddings_initializer=embedding_matrix, mask_zero=True, name="layer_user_review_embedding")
l_item_review_embedding = layers.Embedding(self.n_vocab, self.embedding_size, embeddings_initializer=embedding_matrix, mask_zero=True, name="layer_item_review_embedding")
l_user_embedding = layers.Embedding(self.n_users, self.id_embedding_size, embeddings_initializer="uniform", name="user_embedding")
l_item_embedding = layers.Embedding(self.n_items, self.id_embedding_size, embeddings_initializer="uniform", name="item_embedding")

user_bias = layers.Embedding(self.n_users, 1, embeddings_initializer=tf.initializers.Constant(0.1), name="user_bias")
item_bias = layers.Embedding(self.n_items, 1, embeddings_initializer=tf.initializers.Constant(0.1), name="item_bias")

user_text_processor = TextProcessor(self.max_text_length, filters=self.n_filters, kernel_sizes=self.kernel_sizes, dropout_rate=self.dropout_rate, name='user_text_processor')
item_text_processor = TextProcessor(self.max_text_length, filters=self.n_filters, kernel_sizes=self.kernel_sizes, dropout_rate=self.dropout_rate, name='item_text_processor')

user_review_h = user_text_processor(l_user_review_embedding(i_user_review))
item_review_h = item_text_processor(l_item_review_embedding(i_item_review))
self.graph = Model(
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This is more like a build_graph function inside HDRModel class than initializing a new instance. We only build this graph once for each HDR model, don't we?

self.n_users, self.n_items, self.n_vocab, self.global_mean, embedding_matrix,
self.n_factors, self.embedding_size, self.id_embedding_size,
self.attention_size, self.kernel_sizes, self.n_filters,
self.n_user_mlp_factors, self.n_item_mlp_factors,
self.dropout_rate, self.max_text_length
)

l_user_mlp = keras.models.Sequential([
layers.Dense(self.n_user_mlp_factors, input_dim=self.n_items, activation="relu"),
layers.Dense(self.n_user_mlp_factors // 2, activation="relu"),
layers.Dense(self.n_filters, activation="relu"),
layers.BatchNormalization(),
])
l_item_mlp = keras.models.Sequential([
layers.Dense(self.n_item_mlp_factors, input_dim=self.n_users, activation="relu"),
layers.Dense(self.n_item_mlp_factors // 2, activation="relu"),
layers.Dense(self.n_filters, activation="relu"),
layers.BatchNormalization(),
])
user_rating_h = l_user_mlp(i_user_rating)
item_rating_h = l_item_mlp(i_item_rating)
# mlp
a_user = layers.Dense(1, activation=None, use_bias=True)(
layers.Dense(self.attention_size, activation="relu", use_bias=True)(
def get_weights(self, train_set, batch_size=64):
P = np.zeros((self.n_users, self.n_filters + self.n_factors + self.id_embedding_size))
Q = np.zeros((self.n_items, self.n_filters + self.n_factors + self.id_embedding_size))
A = np.zeros((self.n_items, self.max_num_review))
for batch_users in train_set.user_iter(batch_size, shuffle=False):
i_user_review, i_user_num_reviews, i_user_rating = get_data(batch_users, train_set, self.max_text_length, by='user', max_num_review=self.max_num_review)
user_review_embedding = self.graph.l_user_review_embedding(i_user_review)
user_review_h = self.graph.user_text_processor(user_review_embedding, training=False)
user_rating_h = self.graph.l_user_mlp(i_user_rating)
a_user = self.graph.a_user(
tf.multiply(
user_review_h,
tf.expand_dims(user_rating_h, 1)
)
)
)
a_user_masking = tf.expand_dims(tf.sequence_mask(tf.reshape(i_user_num_reviews, [-1]), maxlen=i_user_review.shape[1]), -1)
user_attention = layers.Softmax(axis=1, name="user_attention")(a_user, a_user_masking)

a_item = layers.Dense(1, activation=None, use_bias=True)(
layers.Dense(self.attention_size, activation="relu", use_bias=True)(
a_user_masking = tf.expand_dims(tf.sequence_mask(tf.reshape(i_user_num_reviews, [-1]), maxlen=i_user_review.shape[1]), -1)
user_attention = self.graph.user_attention(a_user, a_user_masking)
ou = self.graph.ou(tf.reduce_sum(tf.multiply(user_attention, user_review_h), 1))
pu = tf.concat([
user_rating_h,
ou,
self.graph.l_user_embedding(batch_users)
], axis=-1)
P[batch_users] = pu.numpy()
for batch_items in train_set.item_iter(batch_size, shuffle=False):
i_item_review, i_item_num_reviews, i_item_rating = get_data(batch_items, train_set, self.max_text_length, by='item', max_num_review=self.max_num_review)
item_review_embedding = self.graph.l_item_review_embedding(i_item_review)
item_review_h = self.graph.item_text_processor(item_review_embedding, training=False)
item_rating_h = self.graph.l_item_mlp(i_item_rating)
a_item = self.graph.a_item(
tf.multiply(
item_review_h,
tf.expand_dims(item_rating_h, 1)
)
)
)
a_item_masking = tf.expand_dims(tf.sequence_mask(tf.reshape(i_item_num_reviews, [-1]), maxlen=i_item_review.shape[1]), -1)
item_attention = layers.Softmax(axis=1, name="item_attention")(a_item, a_item_masking)

ou = layers.Dense(self.n_factors, use_bias=True, name="ou")(
layers.Dropout(rate=self.dropout_rate)(
tf.reduce_sum(layers.Multiply()([user_attention, user_review_h]), 1)
)
)
oi = layers.Dense(self.n_factors, use_bias=True, name="oi")(
layers.Dropout(rate=self.dropout_rate)(
tf.reduce_sum(layers.Multiply()([item_attention, item_review_h]), 1)
)
)

pu = layers.Concatenate(axis=-1, name="pu")([
tf.expand_dims(user_rating_h, 1),
tf.expand_dims(ou, axis=1),
l_user_embedding(i_user_id)
])

qi = layers.Concatenate(axis=-1, name="qi")([
tf.expand_dims(item_rating_h, 1),
tf.expand_dims(oi, axis=1),
l_item_embedding(i_item_id)
])

W1 = layers.Dense(1, activation=None, use_bias=False, name="W1")
add_global_bias = AddGlobalBias(init_value=self.global_mean, name="global_bias")
r = layers.Add(name="prediction")([
W1(tf.multiply(pu, qi)),
user_bias(i_user_id),
item_bias(i_item_id)
])
r = add_global_bias(r)
self.graph = keras.Model(inputs=[i_user_id, i_item_id, i_user_rating, i_user_review, i_user_num_reviews, i_item_rating, i_item_review, i_item_num_reviews], outputs=r)
if self.verbose:
self.graph.summary()

def get_weights(self, train_set, batch_size=64, max_num_review=32):
user_attention_review_pooling = keras.Model(inputs=[self.graph.get_layer('input_user_id').input, self.graph.get_layer('input_user_rating').input, self.graph.get_layer('input_user_review').input, self.graph.get_layer('input_user_number_of_review').input], outputs=self.graph.get_layer('pu').output)
item_attention_pooling = keras.Model(inputs=[self.graph.get_layer('input_item_id').input, self.graph.get_layer('input_item_rating').input, self.graph.get_layer('input_item_review').input, self.graph.get_layer('input_item_number_of_review').input], outputs=[self.graph.get_layer('qi').output, self.graph.get_layer('item_attention').output])
P = np.zeros((self.n_users, self.n_filters + self.n_factors + self.id_embedding_size), dtype=np.float32)
Q = np.zeros((self.n_items, self.n_filters + self.n_factors + self.id_embedding_size), dtype=np.float32)
A = np.zeros((self.n_items, max_num_review), dtype=np.float32)
for batch_users in train_set.user_iter(batch_size):
user_reviews, user_num_reviews, user_ratings = get_data(batch_users, train_set, self.max_text_length, by='user', max_num_review=max_num_review)
pu = user_attention_review_pooling([batch_users, user_ratings, user_reviews, user_num_reviews], training=False)
P[batch_users] = pu.numpy().reshape(len(batch_users), self.n_filters + self.n_factors + self.id_embedding_size)
for batch_items in train_set.item_iter(batch_size):
item_reviews, item_num_reviews, item_ratings = get_data(batch_items, train_set, self.max_text_length, by='item', max_num_review=max_num_review)
qi, item_attention = item_attention_pooling([batch_items, item_ratings, item_reviews, item_num_reviews], training=False)
Q[batch_items] = qi.numpy().reshape(len(batch_items), self.n_filters + self.n_factors + self.id_embedding_size)
a_item_masking = tf.expand_dims(tf.sequence_mask(tf.reshape(i_item_num_reviews, [-1]), maxlen=i_item_review.shape[1]), -1)
item_attention = self.graph.item_attention(a_item, a_item_masking)
oi = self.graph.oi(tf.reduce_sum(tf.multiply(item_attention, item_review_h), 1))
qi = tf.concat([
item_rating_h,
oi,
self.graph.l_item_embedding(batch_items)
], axis=-1)
Q[batch_items] = qi.numpy()
A[batch_items, :item_attention.shape[1]] = item_attention.numpy().reshape(item_attention.shape[:2])
W1 = self.graph.get_layer('W1').get_weights()[0]
bu = self.graph.get_layer('user_bias').get_weights()[0]
bi = self.graph.get_layer('item_bias').get_weights()[0]
mu = self.graph.get_layer('global_bias').get_weights()[0][0]
W1 = self.graph.W1.get_weights()[0]
bu = self.graph.user_bias.get_weights()[0]
bi = self.graph.item_bias.get_weights()[0]
mu = self.graph.add_global_bias.get_weights()[0][0]
return P, Q, W1, bu, bi, mu, A
Loading