"""
Module description:
"""
__version__ = '0.3.1'
__author__ = 'Vito Walter Anelli, Claudio Pomo, Daniele Malitesta, Antonio Ferrara'
__email__ = 'vitowalter.anelli@poliba.it, claudio.pomo@poliba.it,' \
'daniele.malitesta@poliba.it, antonio.ferrara@poliba.it'
import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
[docs]class DeepFMModel(keras.Model):
def __init__(self,
num_users,
num_items,
embed_mf_size,
hidden_layers,
lambda_weights,
learning_rate=0.01,
random_seed=42,
name="DeepFM",
**kwargs):
super().__init__(name=name, **kwargs)
tf.random.set_seed(random_seed)
self.num_users = num_users
self.num_items = num_items
self.embed_mf_size = embed_mf_size
self.hidden_layers = hidden_layers # Specify as ((5, 'sigmoid'), (10, 'relu'))
self.lambda_weights = lambda_weights
self.initializer = tf.initializers.GlorotUniform()
self.user_mf_embedding = keras.layers.Embedding(input_dim=self.num_users, output_dim=self.embed_mf_size,
embeddings_initializer=self.initializer, name='U_MF',
embeddings_regularizer=keras.regularizers.l2(
self.lambda_weights),
dtype=tf.float32)
self.item_mf_embedding = keras.layers.Embedding(input_dim=self.num_items, output_dim=self.embed_mf_size,
embeddings_regularizer=keras.regularizers.l2(
self.lambda_weights),
embeddings_initializer=self.initializer, name='I_MF',
dtype=tf.float32)
self.u_bias = keras.layers.Embedding(input_dim=self.num_users, output_dim=1,
embeddings_initializer=self.initializer, name='B_U_MF',
dtype=tf.float32)
self.i_bias = keras.layers.Embedding(input_dim=self.num_items, output_dim=1,
embeddings_initializer=self.initializer, name='B_I_MF',
dtype=tf.float32)
self.bias_ = tf.Variable(0., name='GB')
self.user_mf_embedding(0)
self.item_mf_embedding(0)
self.u_bias(0)
self.i_bias(0)
self.hidden = tf.keras.Sequential(
[tf.keras.layers.Dense(self.hidden_layers[0][0],
activation=self.hidden_layers[0][1], input_dim=2*self.embed_mf_size)] +
[tf.keras.layers.Dense(n, activation=act) for n, act in self.hidden_layers[1:]]
)
self.prediction_layer = tf.keras.layers.Dense(1, input_dim=self.hidden_layers[-1][0], activation='sigmoid')
self.loss = keras.losses.BinaryCrossentropy()
self.optimizer = tf.optimizers.Adam(learning_rate)
[docs] @tf.function
def call(self, inputs, training=None, mask=None):
user, item = inputs
user_mf_e = self.user_mf_embedding(user)
item_mf_e = self.item_mf_embedding(item)
fm_output = tf.reduce_sum(user_mf_e * item_mf_e, axis=-1)
fm_output += self.bias_ + self.u_bias(user) + self.i_bias(item)
nn_input = tf.concat([user_mf_e, item_mf_e], axis=-1)
hidden_output = self.hidden(nn_input)
nn_output = self.prediction_layer(hidden_output)
return tf.sigmoid(fm_output + nn_output)
[docs] @tf.function
def train_step(self, batch):
user, pos, label = batch
with tf.GradientTape() as tape:
# Clean Inference
output = self(inputs=(user, pos), training=True)
loss = self.loss(label, output)
grads = tape.gradient(loss, self.trainable_weights)
self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
return loss
[docs] @tf.function
def predict(self, inputs, training=False, **kwargs):
"""
Get full predictions on the whole users/items matrix.
Returns:
The matrix of predicted values.
"""
output = self.call(inputs=inputs, training=training)
return output
[docs] @tf.function
def get_recs(self, inputs, training=False, **kwargs):
"""
Get full predictions on the whole users/items matrix.
Returns:
The matrix of predicted values.
"""
user, item = inputs
user_mf_e = self.user_mf_embedding(user)
item_mf_e = self.item_mf_embedding(item)
fm_output = tf.expand_dims(tf.reduce_sum(user_mf_e * item_mf_e, axis=-1), -1)
fm_output += self.bias_ + self.u_bias(user) + self.i_bias(item)
nn_input = tf.concat([user_mf_e, item_mf_e], axis=-1)
hidden_output = self.hidden(nn_input)
nn_output = self.prediction_layer(hidden_output)
return tf.squeeze(tf.sigmoid(fm_output + nn_output))
[docs] @tf.function
def get_top_k(self, preds, train_mask, k=100):
return tf.nn.top_k(tf.where(train_mask, preds, -np.inf), k=k, sorted=True)