"""Implementation of DeepFM."""
from ..bases import ModelMeta, TfBase
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_sparse_indices,
dense_field_size,
sparse_feat_size,
sparse_field_size,
)
[docs]class DeepFM(TfBase, metaclass=ModelMeta):
"""*DeepFM* algorithm.
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'}, 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``.
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
----------
*Huifeng Guo et al.* `DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
<https://arxiv.org/pdf/1703.04247.pdf>`_.
"""
user_variables = ["linear_user_feat", "embed_user_feat"]
item_variables = ["linear_item_feat", "embed_item_feat"]
sparse_variables = ["linear_sparse_feat", "embed_sparse_feat"]
dense_variables = ["linear_dense_feat", "embed_dense_feat"]
def __init__(
self,
task,
data_info,
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),
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)
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.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.labels = tf.placeholder(tf.float32, shape=[None])
self.is_training = tf.placeholder_with_default(False, shape=[])
self.linear_embed, self.pairwise_embed, self.deep_embed = [], [], []
self._build_user_item()
if self.sparse:
self._build_sparse()
if self.dense:
self._build_dense()
linear_embed = tf.concat(self.linear_embed, axis=1)
pairwise_embed = tf.concat(self.pairwise_embed, axis=1)
deep_embed = tf.concat(self.deep_embed, axis=1)
linear_term = tf_dense(units=1, activation=None)(linear_embed)
pairwise_term = 0.5 * tf.subtract(
tf.square(tf.reduce_sum(pairwise_embed, axis=1)),
tf.reduce_sum(tf.square(pairwise_embed), axis=1),
)
deep_term = dense_nn(
deep_embed,
self.hidden_units,
use_bn=self.use_bn,
dropout_rate=self.dropout_rate,
is_training=self.is_training,
)
concat_layer = tf.concat([linear_term, pairwise_term, deep_term], axis=1)
self.output = tf.squeeze(tf_dense(units=1, activation=None)(concat_layer))
self.serving_topk = self.build_topk(self.output)
count_params()
def _build_user_item(self):
self.user_indices = tf.placeholder(tf.int32, shape=[None])
self.item_indices = tf.placeholder(tf.int32, shape=[None])
linear_user_feat = tf.get_variable(
name="linear_user_feat",
shape=[self.n_users + 1, 1],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
linear_item_feat = tf.get_variable(
name="linear_item_feat",
shape=[self.n_items + 1, 1],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
embed_user_feat = tf.get_variable(
name="embed_user_feat",
shape=[self.n_users + 1, self.embed_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
embed_item_feat = tf.get_variable(
name="embed_item_feat",
shape=[self.n_items + 1, self.embed_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
linear_user_embed = tf.nn.embedding_lookup(linear_user_feat, self.user_indices)
linear_item_embed = tf.nn.embedding_lookup(linear_item_feat, self.item_indices)
self.linear_embed.extend([linear_user_embed, linear_item_embed])
pairwise_user_embed = tf.expand_dims(
tf.nn.embedding_lookup(embed_user_feat, self.user_indices), axis=1
)
pairwise_item_embed = tf.expand_dims(
tf.nn.embedding_lookup(embed_item_feat, self.item_indices), axis=1
)
self.pairwise_embed.extend([pairwise_user_embed, pairwise_item_embed])
deep_user_embed = tf.nn.embedding_lookup(embed_user_feat, self.user_indices)
deep_item_embed = tf.nn.embedding_lookup(embed_item_feat, self.item_indices)
self.deep_embed.extend([deep_user_embed, deep_item_embed])
def _build_sparse(self):
self.sparse_indices = tf.placeholder(
tf.int32, shape=[None, self.sparse_field_size]
)
linear_sparse_feat = tf.get_variable(
name="linear_sparse_feat",
shape=[self.sparse_feature_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
embed_sparse_feat = tf.get_variable(
name="embed_sparse_feat",
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",
):
linear_sparse_embed = multi_sparse_combine_embedding(
self.data_info,
linear_sparse_feat,
self.sparse_indices,
self.multi_sparse_combiner,
embed_size=1,
)
pairwise_sparse_embed = multi_sparse_combine_embedding(
self.data_info,
embed_sparse_feat,
self.sparse_indices,
self.multi_sparse_combiner,
self.embed_size,
)
else:
linear_sparse_embed = tf.nn.embedding_lookup( # B * F1
linear_sparse_feat, self.sparse_indices
)
pairwise_sparse_embed = tf.nn.embedding_lookup( # B * F1 * K
embed_sparse_feat, self.sparse_indices
)
deep_sparse_embed = tf.reshape(
pairwise_sparse_embed, [-1, self.true_sparse_field_size * self.embed_size]
)
self.linear_embed.append(linear_sparse_embed)
self.pairwise_embed.append(pairwise_sparse_embed)
self.deep_embed.append(deep_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]
linear_dense_feat = tf.get_variable(
name="linear_dense_feat",
shape=[self.dense_field_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
embed_dense_feat = tf.get_variable(
name="embed_dense_feat",
shape=[self.dense_field_size, self.embed_size],
initializer=tf.glorot_uniform_initializer(),
regularizer=self.reg,
)
# B * F2
linear_dense_embed = tf.tile(linear_dense_feat, [batch_size])
linear_dense_embed = tf.reshape(linear_dense_embed, [-1, self.dense_field_size])
linear_dense_embed = tf.multiply(linear_dense_embed, self.dense_values)
pairwise_dense_embed = tf.expand_dims(embed_dense_feat, axis=0)
# B * F2 * K
pairwise_dense_embed = tf.tile(pairwise_dense_embed, [batch_size, 1, 1])
pairwise_dense_embed = tf.multiply(pairwise_dense_embed, dense_values_reshape)
deep_dense_embed = tf.reshape(
pairwise_dense_embed, [-1, self.dense_field_size * self.embed_size]
)
self.linear_embed.append(linear_dense_embed)
self.pairwise_embed.append(pairwise_dense_embed)
self.deep_embed.append(deep_dense_embed)