"""
Module description:
"""
__version__ = '0.3.1'
__author__ = 'Felice Antonio Merra, Vito Walter Anelli, Claudio Pomo'
__email__ = 'felice.merra@poliba.it, vitowalter.anelli@poliba.it, claudio.pomo@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 Encoder(tf.keras.layers.Layer):
def __init__(self, hidden_neuron=200,
regularization=0.01,
name="encoder",
**kwargs):
super().__init__(name=name, **kwargs)
self.dense = tf.keras.layers.Dense(hidden_neuron,
activation="sigmoid",
kernel_initializer=keras.initializers.GlorotNormal(),
kernel_regularizer=keras.regularizers.l2(regularization),
bias_initializer=keras.initializers.Ones())
[docs] @tf.function
def call(self, inputs, training=None):
x = self.dense(inputs)
return x
[docs]class Decoder(tf.keras.layers.Layer):
def __init__(self, num_users, name="decoder", regularization=0.01, **kwargs):
super().__init__(name=name, **kwargs)
self.dense_output = tf.keras.layers.Dense(num_users,
activation='sigmoid',
kernel_initializer=keras.initializers.GlorotNormal(),
kernel_regularizer=keras.regularizers.l2(regularization),
bias_initializer=keras.initializers.Ones())
[docs] @tf.function
def call(self, inputs, **kwargs):
x = self.dense_output(inputs)
return x
[docs]class ItemAutoRecModel(keras.Model):
def __init__(self,
data,
num_users,
num_items,
lr,
hidden_neuron,
l_w,
random_seed=42,
name="ItemAutoRec",
**kwargs):
super().__init__(name=name, **kwargs)
tf.random.set_seed(random_seed)
self.data = data
self.num_users = num_users
self.num_items = num_items
self.lr = lr
self.hidden_neuron = hidden_neuron
self.l_w = l_w
self.encoder = Encoder(hidden_neuron=self.hidden_neuron,
regularization=self.l_w
)
self.decoder = Decoder(num_users=self.num_users,
regularization=self.l_w)
self.optimizer = tf.optimizers.Adam(self.lr)
[docs] def get_config(self):
raise NotImplementedError
[docs] @tf.function
def call(self, inputs, training=None, **kwargs):
encoded = self.encoder(inputs, training=training)
reconstructed = self.decoder(encoded)
return reconstructed
[docs] @tf.function
def train_step(self, batch):
with tf.GradientTape() as tape:
# Clean Inference
reconstructed = self.call(inputs=batch, training=True)
# Observing the contribution of only training ratings
reconstructed = reconstructed * tf.sign(batch)
# error
error = batch - reconstructed
loss = tf.reduce_mean(tf.reduce_sum(error**2, axis=1))
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.
"""
scores = self.call(inputs=inputs, training=training)
return scores
[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)
[docs] @tf.function
def get_recs(self, inputs, training=False, **kwargs):
"""
Get full predictions on the whole users/items matrix. [Is Inverted]
Returns:
The matrix of predicted values.
"""
return self.predict(inputs)