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* Add HRDR model * refactor code * refactor code to use save and load function
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from .recom_hrdr import HRDR |
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import numpy as np | ||
import tensorflow as tf | ||
from tensorflow import keras | ||
from tensorflow.keras import layers, initializers | ||
from tensorflow.python.keras.preprocessing.sequence import pad_sequences | ||
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from ...utils import get_rng | ||
from ...utils.init_utils import uniform | ||
from ..narre.narre import TextProcessor, AddGlobalBias | ||
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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 | ||
if by == "user" | ||
else train_set.review_text.item_review | ||
) | ||
for idx in batch_ids: | ||
review_ids = [] | ||
for inc, (jdx, review_idx) in enumerate(review_group[idx].items()): | ||
if max_num_review is not None and inc == max_num_review: | ||
break | ||
review_ids.append(review_idx) | ||
reviews = train_set.review_text.batch_seq( | ||
review_ids, max_length=max_text_length | ||
) | ||
batch_reviews.append(reviews) | ||
batch_num_reviews.append(len(reviews)) | ||
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) | ||
if by == "user" | ||
else np.zeros((len(batch_ids), train_set.num_users), dtype=np.float32) | ||
) | ||
rating_group = train_set.user_data if by == "user" else train_set.item_data | ||
for batch_inc, idx in enumerate(batch_ids): | ||
jds, ratings = rating_group[idx] | ||
for jdx, rating in zip(jds, ratings): | ||
batch_ratings[batch_inc, jdx] = rating | ||
return batch_reviews, batch_num_reviews, batch_ratings | ||
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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') | ||
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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") | ||
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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 | ||
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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, max_num_review=32, | ||
pretrained_word_embeddings=None, verbose=False, seed=None): | ||
self.n_users = n_users | ||
self.n_items = n_items | ||
self.n_vocab = vocab.size | ||
self.global_mean = global_mean | ||
self.n_factors = n_factors | ||
self.embedding_size = embedding_size | ||
self.id_embedding_size = id_embedding_size | ||
self.attention_size = attention_size | ||
self.kernel_sizes = kernel_sizes | ||
self.n_filters = n_filters | ||
self.n_user_mlp_factors = n_user_mlp_factors | ||
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) | ||
tf.random.set_seed(seed) | ||
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embedding_matrix = uniform(shape=(self.n_vocab, self.embedding_size), low=-0.5, high=0.5, random_state=self.rng) | ||
embedding_matrix[:4, :] = np.zeros((4, self.embedding_size)) | ||
if pretrained_word_embeddings is not None: | ||
oov_count = 0 | ||
for word, idx in vocab.tok2idx.items(): | ||
embedding_vector = pretrained_word_embeddings.get(word) | ||
if embedding_vector is not None: | ||
embedding_matrix[idx] = embedding_vector | ||
else: | ||
oov_count += 1 | ||
if self.verbose: | ||
print("Number of OOV words: %d" % oov_count) | ||
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embedding_matrix = initializers.Constant(embedding_matrix) | ||
self.graph = Model( | ||
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 | ||
) | ||
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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 = 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 = 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.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 |
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