"""Implementation of YouTubeRanking."""
import numpy as np
from ..bases import ModelMeta, TfBase
from ..batch.sequence import get_recent_seqs
from ..feature.multi_sparse import true_sparse_field_size
from ..tfops import (
dense_nn,
dropout_config,
multi_sparse_combine_embedding,
reg_config,
tf,
tf_dense,
)
from ..torchops import hidden_units_config
from ..utils.misc import count_params
from ..utils.validate import (
check_dense_values,
check_multi_sparse,
check_seq_mode,
check_sparse_indices,
dense_field_size,
sparse_feat_size,
sparse_field_size,
)
[docs]class YouTubeRanking(TfBase, metaclass=ModelMeta):
"""*YouTubeRanking* algorithm.
.. NOTE::
The algorithm implemented mainly corresponds to the ranking phase
based on the original paper.
.. WARNING::
YouTubeRanking can only be used in `ranking` task.
Parameters
----------
task : {'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'}, default: 'cross_entropy'
Loss for model training.
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.
sampler : {'random', 'unconsumed', 'popular'}, default: 'random'
Negative sampling strategy.
- ``'random'`` means random sampling.
- ``'unconsumed'`` samples items that the target user did not consume before.
- ``'popular'`` has a higher probability to sample popular items as negative samples.
.. versionadded:: 1.1.0
num_neg : int, default: 1
Number of negative samples for each positive sample, only used in `ranking` task.
use_bn : bool, default: True
Whether to use batch normalization.
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: (128, 64, 32)
Number of layers and corresponding layer size in MLP.
.. versionchanged:: 1.0.0
Accept type of ``int``, ``list`` or ``tuple``, instead of ``str``.
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.
multi_sparse_combiner : {'normal', 'mean', 'sum', 'sqrtn'}, default: 'sqrtn'
Options for combining `multi_sparse` features.
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
----------
*Paul Covington et al.* `Deep Neural Networks for YouTube Recommendations
<https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf>`_.
"""
user_variables = ["user_features"]
item_variables = ["item_features"]
sparse_variables = ["sparse_features"]
dense_variables = ["dense_features"]
def __init__(
self,
task="ranking",
data_info=None,
loss_type="cross_entropy",
embed_size=16,
n_epochs=20,
lr=0.001,
lr_decay=False,
epsilon=1e-5,
reg=None,
batch_size=256,
sampler="random",
num_neg=1,
use_bn=True,
dropout_rate=None,
hidden_units=(128, 64, 32),
recent_num=10,
random_num=None,
multi_sparse_combiner="sqrtn",
seed=42,
lower_upper_bound=None,
tf_sess_config=None,
):
super().__init__(task, data_info, lower_upper_bound, tf_sess_config)
assert task == "ranking", "YouTube models is only suitable for ranking"
self.all_args = locals()
self.loss_type = loss_type
self.embed_size = embed_size
self.n_epochs = n_epochs
self.lr = lr
self.lr_decay = lr_decay
self.epsilon = epsilon
self.reg = reg_config(reg)
self.batch_size = batch_size
self.sampler = sampler
self.num_neg = num_neg
self.use_bn = use_bn
self.dropout_rate = dropout_config(dropout_rate)
self.hidden_units = hidden_units_config(hidden_units)
self.seq_mode, self.max_seq_len = check_seq_mode(recent_num, random_num)
self.recent_seqs, self.recent_seq_lens = get_recent_seqs(
self.n_users,
self.user_consumed,
self.n_items,
self.max_seq_len,
dtype=np.float32,
)
self.seed = seed
self.sparse = check_sparse_indices(data_info)
self.dense = check_dense_values(data_info)
if self.sparse:
self.sparse_feature_size = sparse_feat_size(data_info)
self.sparse_field_size = sparse_field_size(data_info)
self.multi_sparse_combiner = check_multi_sparse(
data_info, multi_sparse_combiner
)
self.true_sparse_field_size = true_sparse_field_size(
data_info, self.sparse_field_size, self.multi_sparse_combiner
)
if self.dense:
self.dense_field_size = dense_field_size(data_info)
def build_model(self):
tf.set_random_seed(self.seed)
self.user_indices = tf.placeholder(tf.int32, shape=[None])
self.item_indices = tf.placeholder(tf.int32, shape=[None])
self.user_interacted_seq = tf.placeholder(
tf.int32, shape=[None, self.max_seq_len]
)
self.user_interacted_len = tf.placeholder(tf.float32, shape=[None])
self.labels = tf.placeholder(tf.float32, shape=[None])
self.is_training = tf.placeholder_with_default(False, shape=[])
self.concat_embed = []
user_features = tf.get_variable(
name="user_features",
shape=[self.n_users + 1, self.embed_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
item_features = tf.get_variable(
name="item_features",
shape=[self.n_items + 1, self.embed_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
user_embed = tf.nn.embedding_lookup(user_features, self.user_indices)
item_embed = tf.nn.embedding_lookup(item_features, self.item_indices)
# unknown items are padded to 0-vector
zero_padding_op = tf.scatter_update(
item_features, self.n_items, tf.zeros([self.embed_size], dtype=tf.float32)
)
with tf.control_dependencies([zero_padding_op]):
# B * seq * K
multi_item_embed = tf.nn.embedding_lookup(
item_features, self.user_interacted_seq
)
pooled_embed = tf.div_no_nan(
tf.reduce_sum(multi_item_embed, axis=1),
tf.expand_dims(tf.sqrt(self.user_interacted_len), axis=1),
)
self.concat_embed.extend([user_embed, item_embed, pooled_embed])
if self.sparse:
self._build_sparse()
if self.dense:
self._build_dense()
concat_embed = tf.concat(self.concat_embed, axis=1)
mlp_layer = dense_nn(
concat_embed,
self.hidden_units,
use_bn=self.use_bn,
dropout_rate=self.dropout_rate,
is_training=self.is_training,
)
self.output = tf.reshape(tf_dense(units=1)(mlp_layer), [-1])
self.serving_topk = self.build_topk(self.output)
count_params()
def _build_sparse(self):
self.sparse_indices = tf.placeholder(
tf.int32, shape=[None, self.sparse_field_size]
)
sparse_features = tf.get_variable(
name="sparse_features",
shape=[self.sparse_feature_size, self.embed_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
if self.data_info.multi_sparse_combine_info and self.multi_sparse_combiner in (
"sum",
"mean",
"sqrtn",
):
sparse_embed = multi_sparse_combine_embedding(
self.data_info,
sparse_features,
self.sparse_indices,
self.multi_sparse_combiner,
self.embed_size,
)
else:
sparse_embed = tf.nn.embedding_lookup(sparse_features, self.sparse_indices)
sparse_embed = tf.reshape(
sparse_embed, [-1, self.true_sparse_field_size * self.embed_size]
)
self.concat_embed.append(sparse_embed)
def _build_dense(self):
self.dense_values = tf.placeholder(
tf.float32, shape=[None, self.dense_field_size]
)
dense_values_reshape = tf.reshape(
self.dense_values, [-1, self.dense_field_size, 1]
)
batch_size = tf.shape(self.dense_values)[0]
dense_features = tf.get_variable(
name="dense_features",
shape=[self.dense_field_size, self.embed_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
dense_embed = tf.tile(dense_features, [batch_size, 1])
dense_embed = tf.reshape(
dense_embed, [-1, self.dense_field_size, self.embed_size]
)
dense_embed = tf.multiply(dense_embed, dense_values_reshape)
dense_embed = tf.reshape(
dense_embed, [-1, self.dense_field_size * self.embed_size]
)
self.concat_embed.append(dense_embed)