"""Implementation of RNN4Rec model."""
import numpy as np
from ..bases import EmbedBase, ModelMeta
from ..data.sequence import get_user_last_interacted
from ..tfops import dropout_config, reg_config, sess_config, tf, tf_dense, tf_rnn
from ..torchops import hidden_units_config
from ..utils.misc import count_params
from ..utils.validate import check_seq_mode
[docs]class RNN4Rec(EmbedBase, metaclass=ModelMeta, backend="tensorflow"):
"""*RNN4Rec* algorithm.
.. NOTE::
The original paper used GRU, but in this implementation we can also use LSTM.
Parameters
----------
task : {'rating', 'ranking'}
Recommendation task. See :ref:`Task`.
data_info : :class:`~libreco.data.DataInfo` object
Object that contains useful information for training and inference.
loss_type : {'cross_entropy', 'focal', 'bpr'}, default: 'cross_entropy'
Loss for model training.
rnn_type : {'lstm', 'gru'}, default: 'gru'
RNN for modeling.
embed_size: int, default: 16
Vector size of embeddings.
n_epochs: int, default: 10
Number of epochs for training.
lr : float, default 0.001
Learning rate for training.
lr_decay : bool, default: False
Whether to use learning rate decay.
epsilon : float, default: 1e-5
A small constant added to the denominator to improve numerical stability in
Adam optimizer.
According to the `official comment <https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/adam.py#L64>`_,
default value of `1e-8` for `epsilon` is generally not good, so here we choose `1e-5`.
Users can try tuning this hyperparameter if the training is unstable.
reg : float or None, default: None
Regularization parameter, must be non-negative or None.
batch_size : int, default: 256
Batch size for training.
num_neg : int, default: 1
Number of negative samples for each positive sample, only used in `ranking` task.
dropout_rate : float or None, default: None
Probability of an element to be zeroed. If it is None, dropout is not used.
hidden_units : int, list of int or tuple of (int,), default: 16
Number of layers and corresponding layer size in RNN.
.. versionchanged:: 1.0.0
Accept type of ``int``, ``list`` or ``tuple``, instead of ``str``.
use_layer_norm : bool, default: False
Whether to use layer normalization.
recent_num : int or None, default: 10
Number of recent items to use in user behavior sequence.
random_num : int or None, default: None
Number of random sampled items to use in user behavior sequence.
If `recent_num` is not None, `random_num` is not considered.
seed : int, default: 42
Random seed.
lower_upper_bound : tuple or None, default: None
Lower and upper score bound for `rating` task.
tf_sess_config : dict or None, default: None
Optional TensorFlow session config, see `ConfigProto options
<https://github.com/tensorflow/tensorflow/blob/v2.10.0/tensorflow/core/protobuf/config.proto#L431>`_.
References
----------
*Balazs Hidasi et al.* `Session-based Recommendations with Recurrent Neural Networks
<https://arxiv.org/pdf/1511.06939.pdf>`_.
"""
item_variables = ["item_weights", "item_biases", "input_embed"]
def __init__(
self,
task,
data_info=None,
loss_type="cross_entropy",
rnn_type="gru",
embed_size=16,
n_epochs=20,
lr=0.001,
lr_decay=False,
epsilon=1e-5,
reg=None,
batch_size=256,
num_neg=1,
dropout_rate=None,
hidden_units=16,
use_layer_norm=False,
recent_num=10,
random_num=None,
seed=42,
lower_upper_bound=None,
tf_sess_config=None,
):
super().__init__(task, data_info, embed_size, lower_upper_bound)
self.all_args = locals()
self.sess = sess_config(tf_sess_config)
self.loss_type = loss_type
self.rnn_type = rnn_type.lower()
self.n_epochs = n_epochs
self.lr = lr
self.lr_decay = lr_decay
self.epsilon = epsilon
self.hidden_units = hidden_units_config(hidden_units)
self.reg = reg_config(reg)
self.batch_size = batch_size
self.num_neg = num_neg
self.dropout_rate = dropout_config(dropout_rate)
self.use_ln = use_layer_norm
self.seed = seed
self.seq_mode, self.max_seq_len = check_seq_mode(recent_num, random_num)
self.recent_seqs, self.recent_seq_lens = self._set_recent_seqs()
self._check_params()
def build_model(self):
tf.set_random_seed(self.seed)
self.labels = tf.placeholder(tf.float32, shape=[None])
self.is_training = tf.placeholder_with_default(False, shape=[])
self._build_variables()
self._build_user_embeddings()
if self.task == "rating" or self.loss_type in ("cross_entropy", "focal"):
self.user_indices = tf.placeholder(tf.int32, shape=[None])
self.item_indices = tf.placeholder(tf.int32, shape=[None])
item_vector = tf.nn.embedding_lookup(self.item_weights, self.item_indices)
item_bias = tf.nn.embedding_lookup(self.item_biases, self.item_indices)
self.output = (
tf.reduce_sum(tf.multiply(self.user_vector, item_vector), axis=1)
+ item_bias
)
elif self.loss_type == "bpr":
self.item_indices_pos = tf.placeholder(tf.int32, shape=[None])
self.item_indices_neg = tf.placeholder(tf.int32, shape=[None])
item_embed_pos = tf.nn.embedding_lookup(
self.item_weights, self.item_indices_pos
)
item_embed_neg = tf.nn.embedding_lookup(
self.item_weights, self.item_indices_neg
)
item_bias_pos = tf.nn.embedding_lookup(
self.item_biases, self.item_indices_pos
)
item_bias_neg = tf.nn.embedding_lookup(
self.item_biases, self.item_indices_neg
)
item_diff = tf.subtract(item_bias_pos, item_bias_neg) + tf.reduce_sum(
tf.multiply(
self.user_vector, tf.subtract(item_embed_pos, item_embed_neg)
),
axis=1,
)
self.bpr_loss = tf.log_sigmoid(item_diff)
count_params()
def _build_variables(self):
# weight and bias parameters for last fc_layer
self.item_biases = tf.get_variable(
name="item_biases",
shape=[self.n_items],
initializer=tf.zeros_initializer(),
regularizer=self.reg,
)
self.item_weights = tf.get_variable(
name="item_weights",
shape=[self.n_items, self.embed_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
# input_embed for rnn_layer, include padding value
self.input_embed = tf.get_variable(
name="input_embed",
shape=[self.n_items + 1, self.hidden_units[0]],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
def _build_user_embeddings(self):
self.user_interacted_seq = tf.placeholder(
tf.int32, shape=[None, self.max_seq_len]
)
self.user_interacted_len = tf.placeholder(tf.int64, shape=[None])
seq_item_embed = tf.nn.embedding_lookup(
self.input_embed, self.user_interacted_seq
)
rnn_output = tf_rnn(
inputs=seq_item_embed,
rnn_type=self.rnn_type,
lengths=self.user_interacted_len,
maxlen=self.max_seq_len,
hidden_units=self.hidden_units,
dropout_rate=self.dropout_rate,
use_ln=self.use_ln,
is_training=self.is_training,
)
self.user_vector = tf_dense(units=self.embed_size, activation=None)(rnn_output)
def _set_recent_seqs(self):
recent_seqs, recent_seq_lens = get_user_last_interacted(
self.n_users, self.user_consumed, self.n_items, self.max_seq_len
)
return recent_seqs, recent_seq_lens.astype(np.int64)
def set_embeddings(self):
feed_dict = {
self.user_interacted_seq: self.recent_seqs,
self.user_interacted_len: self.recent_seq_lens,
}
user_vector = self.sess.run(self.user_vector, feed_dict)
item_weights = self.sess.run(self.item_weights)
item_biases = self.sess.run(self.item_biases)
user_bias = np.ones([len(user_vector), 1], dtype=user_vector.dtype)
item_bias = item_biases[:, None]
self.user_embed = np.hstack([user_vector, user_bias])
self.item_embed = np.hstack([item_weights, item_bias])
def _check_params(self):
# assert self.hidden_units[-1] == self.embed_size, (
# "dimension of last rnn hidden unit should equal to embed_size"
# )
assert self.rnn_type in ("lstm", "gru"), "rnn_type must be either lstm or gru"