Module tfrec.models.svd

Expand source code 
import tensorflow as tf
from tensorflow import keras
import numpy as np


class SVD(keras.Model):
    """ **SVD** - A Matrix Factorization Algorithm for Collaborative Filtering.

    As demonstrated by Simon Funk in his
    blogpost https://sifter.org/~simon/journal/20061211.html.

    ...

    Attributes
    -----------
    n_users : int
        Total number of users in the dataset.
    n_items : int
        Total number of items in the dataset.
    global_mean : float
        Mean of ratings in the training set.
    embedding_dim : int, optional
        Number of factors for user and item (default is `50`).
    biased : boolean, optional
        Whether bias should be used or not (default is `True`).
    init_mean : float, optional
        Mean of random initilization for embeddings (default is `0`).
    init_std_dev : float, optional
        Standard deviation of random initilization for embeddings (default is `0.1`).
    reg_all : float, optional
        L2 regularization factor for all trainable variables (default is `0.0001`).
    reg_user_embed : float, optional
        L2 regularization factor for user embedding (default is `reg_all`).
    reg_item_embed : float, optional
        L2 regularization factor for item embedding (default is `reg_all`).
    reg_user_bias : float, optional
        L2 regularization factor for user bias (default is `reg_all`).
    reg_item_bias : float, optional
        L2 regularization factor for item bias (default is `reg_all`).
    random_state : integer, optional
        Seed variable, useful for reproducing results (default is `None`).
    """

    def __init__(self, n_users, n_items, global_mean, embedding_dim=50, biased=True,
                 init_mean=0, init_std_dev=0.1, reg_all=0.0001, reg_user_embed=None,
                 reg_item_embed=None, reg_user_bias=None, reg_item_bias=None, random_state=None, **kwargs):
        """
        Parameters
        -----------
        n_users : int
            Total number of users in the dataset.
        n_items : int
            Total number of items in the dataset.
        global_mean : float
            Mean of ratings in the training set.
        embedding_dim : int, optional
            Number of factors for user and item (default is `50`).
        biased : boolean, optional
            Whether bias should be used or not (default is `True`).
        init_mean : float, optional
            Mean of random initilization for embeddings (default is `0`).
        init_std_dev : float, optional
            Standard deviation of random initilization for embeddings (default is `0.1`).
        reg_all : float, optional
            L2 regularization factor for all trainable variables (default is `0.0001`).
        reg_user_embed : float, optional
            L2 regularization factor for user embedding (default is `reg_all`).
        reg_item_embed : float, optional
            L2 regularization factor for item embedding (default is `reg_all`).
        reg_user_bias : float, optional
            L2 regularization factor for user bias (default is `reg_all`).
        reg_item_bias : float, optional
            L2 regularization factor for item bias (default is `reg_all`).
        random_state : integer, optional
            Seed variable, useful for reproducing results (default is `None`).
        """

        super().__init__(**kwargs)
        self.n_users = n_users
        self.n_items = n_items
        self.global_mean = global_mean.astype(np.float32)
        self.embedding_dim = embedding_dim
        self.biased = biased
        self.init_mean = init_mean
        self.init_std_dev = init_std_dev
        self.reg_all = reg_all
        self.reg_user_embed = reg_all if not reg_user_embed else reg_user_embed
        self.reg_item_embed = reg_all if not reg_item_embed else reg_item_embed
        self.reg_user_bias = reg_all if not reg_user_bias else reg_user_bias
        self.reg_item_bias = reg_all if not reg_item_bias else reg_item_bias
        self.random_state = random_state

        self.user_embedding = keras.layers.Embedding(
            input_dim=self.n_users, output_dim=self.embedding_dim,
            embeddings_initializer=tf.keras.initializers.RandomNormal(
                mean=self.init_mean, stddev=self.init_std_dev, seed=self.random_state),
            embeddings_regularizer=tf.keras.regularizers.L2(
                self.reg_user_embed)
        )
        self.item_embedding = keras.layers.Embedding(
            input_dim=self.n_items, output_dim=self.embedding_dim,
            embeddings_initializer=tf.keras.initializers.RandomNormal(
                mean=self.init_mean, stddev=self.init_std_dev, seed=self.random_state),
            embeddings_regularizer=tf.keras.regularizers.L2(
                self.reg_item_embed)
        )
        if self.biased:
            self.user_bias = keras.layers.Embedding(
                input_dim=self.n_users, output_dim=1,
                embeddings_initializer=tf.keras.initializers.Zeros(),
                embeddings_regularizer=tf.keras.regularizers.L2(
                    self.reg_user_bias)
            )
            self.item_bias = keras.layers.Embedding(
                input_dim=self.n_items, output_dim=1,
                embeddings_initializer=tf.keras.initializers.Zeros(),
                embeddings_regularizer=tf.keras.regularizers.L2(
                    self.reg_item_bias)
            )

    def call(self, inputs):
        """Forward pass of input batch."""
        # Separate Inputs
        user, item = inputs[:, 0], inputs[:, 1]

        # Dot Product
        user_embed, item_embed = self.user_embedding(
            user), self.item_embedding(item)
        rating = tf.math.reduce_sum(tf.multiply(
            user_embed, item_embed), 1, keepdims=True)

        # Add global mean and bias if self.bias = True
        if self.biased:
            user_bias, item_bias = self.user_bias(user), self.item_bias(item)
            total_bias = tf.math.add(
                self.global_mean, tf.math.add(user_bias, item_bias))
            rating = tf.math.add(rating, total_bias)

        return rating

Classes

class SVD (n_users, n_items, global_mean, embedding_dim=50, biased=True, init_mean=0, init_std_dev=0.1, reg_all=0.0001, reg_user_embed=None, reg_item_embed=None, reg_user_bias=None, reg_item_bias=None, random_state=None, **kwargs)

SVD - A Matrix Factorization Algorithm for Collaborative Filtering.

As demonstrated by Simon Funk in his blogpost https://sifter.org/~simon/journal/20061211.html.

…

Attributes

n_users : int
Total number of users in the dataset.
n_items : int
Total number of items in the dataset.
global_mean : float
Mean of ratings in the training set.
embedding_dim : int, optional
Number of factors for user and item (default is 50).
biased : boolean, optional
Whether bias should be used or not (default is True).
init_mean : float, optional
Mean of random initilization for embeddings (default is 0).
init_std_dev : float, optional
Standard deviation of random initilization for embeddings (default is 0.1).
reg_all : float, optional
L2 regularization factor for all trainable variables (default is 0.0001).
reg_user_embed : float, optional
L2 regularization factor for user embedding (default is reg_all).
reg_item_embed : float, optional
L2 regularization factor for item embedding (default is reg_all).
reg_user_bias : float, optional
L2 regularization factor for user bias (default is reg_all).
reg_item_bias : float, optional
L2 regularization factor for item bias (default is reg_all).
random_state : integer, optional
Seed variable, useful for reproducing results (default is None).

Parameters

n_users : int
Total number of users in the dataset.
n_items : int
Total number of items in the dataset.
global_mean : float
Mean of ratings in the training set.
embedding_dim : int, optional
Number of factors for user and item (default is 50).
biased : boolean, optional
Whether bias should be used or not (default is True).
init_mean : float, optional
Mean of random initilization for embeddings (default is 0).
init_std_dev : float, optional
Standard deviation of random initilization for embeddings (default is 0.1).
reg_all : float, optional
L2 regularization factor for all trainable variables (default is 0.0001).
reg_user_embed : float, optional
L2 regularization factor for user embedding (default is reg_all).
reg_item_embed : float, optional
L2 regularization factor for item embedding (default is reg_all).
reg_user_bias : float, optional
L2 regularization factor for user bias (default is reg_all).
reg_item_bias : float, optional
L2 regularization factor for item bias (default is reg_all).
random_state : integer, optional
Seed variable, useful for reproducing results (default is None).
Expand source code 
class SVD(keras.Model):
    """ **SVD** - A Matrix Factorization Algorithm for Collaborative Filtering.

    As demonstrated by Simon Funk in his
    blogpost https://sifter.org/~simon/journal/20061211.html.

    ...

    Attributes
    -----------
    n_users : int
        Total number of users in the dataset.
    n_items : int
        Total number of items in the dataset.
    global_mean : float
        Mean of ratings in the training set.
    embedding_dim : int, optional
        Number of factors for user and item (default is `50`).
    biased : boolean, optional
        Whether bias should be used or not (default is `True`).
    init_mean : float, optional
        Mean of random initilization for embeddings (default is `0`).
    init_std_dev : float, optional
        Standard deviation of random initilization for embeddings (default is `0.1`).
    reg_all : float, optional
        L2 regularization factor for all trainable variables (default is `0.0001`).
    reg_user_embed : float, optional
        L2 regularization factor for user embedding (default is `reg_all`).
    reg_item_embed : float, optional
        L2 regularization factor for item embedding (default is `reg_all`).
    reg_user_bias : float, optional
        L2 regularization factor for user bias (default is `reg_all`).
    reg_item_bias : float, optional
        L2 regularization factor for item bias (default is `reg_all`).
    random_state : integer, optional
        Seed variable, useful for reproducing results (default is `None`).
    """

    def __init__(self, n_users, n_items, global_mean, embedding_dim=50, biased=True,
                 init_mean=0, init_std_dev=0.1, reg_all=0.0001, reg_user_embed=None,
                 reg_item_embed=None, reg_user_bias=None, reg_item_bias=None, random_state=None, **kwargs):
        """
        Parameters
        -----------
        n_users : int
            Total number of users in the dataset.
        n_items : int
            Total number of items in the dataset.
        global_mean : float
            Mean of ratings in the training set.
        embedding_dim : int, optional
            Number of factors for user and item (default is `50`).
        biased : boolean, optional
            Whether bias should be used or not (default is `True`).
        init_mean : float, optional
            Mean of random initilization for embeddings (default is `0`).
        init_std_dev : float, optional
            Standard deviation of random initilization for embeddings (default is `0.1`).
        reg_all : float, optional
            L2 regularization factor for all trainable variables (default is `0.0001`).
        reg_user_embed : float, optional
            L2 regularization factor for user embedding (default is `reg_all`).
        reg_item_embed : float, optional
            L2 regularization factor for item embedding (default is `reg_all`).
        reg_user_bias : float, optional
            L2 regularization factor for user bias (default is `reg_all`).
        reg_item_bias : float, optional
            L2 regularization factor for item bias (default is `reg_all`).
        random_state : integer, optional
            Seed variable, useful for reproducing results (default is `None`).
        """

        super().__init__(**kwargs)
        self.n_users = n_users
        self.n_items = n_items
        self.global_mean = global_mean.astype(np.float32)
        self.embedding_dim = embedding_dim
        self.biased = biased
        self.init_mean = init_mean
        self.init_std_dev = init_std_dev
        self.reg_all = reg_all
        self.reg_user_embed = reg_all if not reg_user_embed else reg_user_embed
        self.reg_item_embed = reg_all if not reg_item_embed else reg_item_embed
        self.reg_user_bias = reg_all if not reg_user_bias else reg_user_bias
        self.reg_item_bias = reg_all if not reg_item_bias else reg_item_bias
        self.random_state = random_state

        self.user_embedding = keras.layers.Embedding(
            input_dim=self.n_users, output_dim=self.embedding_dim,
            embeddings_initializer=tf.keras.initializers.RandomNormal(
                mean=self.init_mean, stddev=self.init_std_dev, seed=self.random_state),
            embeddings_regularizer=tf.keras.regularizers.L2(
                self.reg_user_embed)
        )
        self.item_embedding = keras.layers.Embedding(
            input_dim=self.n_items, output_dim=self.embedding_dim,
            embeddings_initializer=tf.keras.initializers.RandomNormal(
                mean=self.init_mean, stddev=self.init_std_dev, seed=self.random_state),
            embeddings_regularizer=tf.keras.regularizers.L2(
                self.reg_item_embed)
        )
        if self.biased:
            self.user_bias = keras.layers.Embedding(
                input_dim=self.n_users, output_dim=1,
                embeddings_initializer=tf.keras.initializers.Zeros(),
                embeddings_regularizer=tf.keras.regularizers.L2(
                    self.reg_user_bias)
            )
            self.item_bias = keras.layers.Embedding(
                input_dim=self.n_items, output_dim=1,
                embeddings_initializer=tf.keras.initializers.Zeros(),
                embeddings_regularizer=tf.keras.regularizers.L2(
                    self.reg_item_bias)
            )

    def call(self, inputs):
        """Forward pass of input batch."""
        # Separate Inputs
        user, item = inputs[:, 0], inputs[:, 1]

        # Dot Product
        user_embed, item_embed = self.user_embedding(
            user), self.item_embedding(item)
        rating = tf.math.reduce_sum(tf.multiply(
            user_embed, item_embed), 1, keepdims=True)

        # Add global mean and bias if self.bias = True
        if self.biased:
            user_bias, item_bias = self.user_bias(user), self.item_bias(item)
            total_bias = tf.math.add(
                self.global_mean, tf.math.add(user_bias, item_bias))
            rating = tf.math.add(rating, total_bias)

        return rating

Ancestors

  • tensorflow.python.keras.engine.training.Model
  • tensorflow.python.keras.engine.base_layer.Layer
  • tensorflow.python.module.module.Module
  • tensorflow.python.training.tracking.tracking.AutoTrackable
  • tensorflow.python.training.tracking.base.Trackable
  • tensorflow.python.keras.utils.version_utils.LayerVersionSelector
  • tensorflow.python.keras.utils.version_utils.ModelVersionSelector

Methods

def call(self, inputs)

Forward pass of input batch.

Expand source code 
def call(self, inputs):
    """Forward pass of input batch."""
    # Separate Inputs
    user, item = inputs[:, 0], inputs[:, 1]

    # Dot Product
    user_embed, item_embed = self.user_embedding(
        user), self.item_embedding(item)
    rating = tf.math.reduce_sum(tf.multiply(
        user_embed, item_embed), 1, keepdims=True)

    # Add global mean and bias if self.bias = True
    if self.biased:
        user_bias, item_bias = self.user_bias(user), self.item_bias(item)
        total_bias = tf.math.add(
            self.global_mean, tf.math.add(user_bias, item_bias))
        rating = tf.math.add(rating, total_bias)

    return rating