mojolearn.AlternatingLeastSquares#

class mojolearn.AlternatingLeastSquares(factors=100, regularization=0.01, alpha=1.0, iterations=15, use_cg=False, calculate_training_loss=False, random_state=None, numeric_mode=None, cg_steps=3)[source]#

Bases: _Base

Implicit-feedback matrix factorization by alternating least squares.

The method of Hu, Koren and Volinsky (2008), following the implicit library: confidence is alpha * user_items, and each iteration solves for every user’s factors and then every item’s. Unlike implicit, use_cg defaults to False (the exact solve), and user_items is held dense (0 means no interaction; a scipy.sparse matrix is densified).

Parameters:

factors (int, default 100) – Number of latent factors.
regularization (float, default 0.01) – L2 penalty on the factors.
alpha (float, default 1.0) – Confidence scale applied to user_items.
iterations (int, default 15) – Number of alternating sweeps.
use_cg (bool, default False) – Use implicit’s conjugate-gradient solver (cg_steps steps per row per half-sweep) instead of the exact Cholesky solve.
calculate_training_loss (bool, default False) – Record training_loss_ after every iteration (implicit’s loss).
random_state (int or None, default None) – Seed of the starting factors; None means 0.
numeric_mode ({“identical”, “fast”} or None, default None) – Numeric mode for this estimator. None uses the package default ("identical" unless changed). "identical" gives the same bits on NVIDIA and AMD GPUs; "fast" applies on Apple GPUs.
cg_steps (int, default 3) – Conjugate-gradient steps per row with use_cg=True; must be at least 1.

Variables:

user_factors_ (user_factors,)
item_factors_ (item_factors,)
training_loss_ (list of float) – Only with calculate_training_loss=True.

Examples

>>> import numpy as np, mojolearn
>>> R = (np.random.default_rng(0).random((200, 50)) < 0.05).astype(np.float32)
>>> als = mojolearn.AlternatingLeastSquares(factors=8, iterations=5, random_state=0).fit(R)
>>> ids, scores = als.recommend(0, R, N=5)

__init__(factors=100, regularization=0.01, alpha=1.0, iterations=15, use_cg=False, calculate_training_loss=False, random_state=None, numeric_mode=None, cg_steps=3)[source]#

Methods

`fit`(user_items[, show_progress])	Fit user and item factors.
`fit_transform`(X[, y])	Fit to X, then return `transform(X)`.
`get_params`([deep])	Return this estimator's constructor parameters.
`recommend`(userid, user_items[, N, ...])	Return the top N items for one user.
`set_params`(**params)	Set constructor parameters by keyword.
`similar_items`(itemid[, N])	Return the N items most similar to one item.

fit(user_items, show_progress=False)[source]#

Fit user and item factors.

Parameters:

user_items (array-like of shape (n_users, n_items)) – Interaction strengths; 0 means no interaction. A scipy.sparse matrix is densified.
show_progress (bool, default False) – Accepted for implicit compatibility; nothing is shown.

Returns:

self

Raises:

ValueError – If use_cg=True and cg_steps < 1.

recommend(userid, user_items, N=10, filter_already_liked_items=True)[source]#

Return the top N items for one user.

Items are ranked by the dot product of user and item factors, ties going to the lower item id.

Parameters:

userid (int) – Row of the user.
user_items (array-like of shape (n_users, n_items) or (1, n_items), or None) – Interactions used to filter items the user already has: row userid, or the single row given.
N (int, default 10) – Number of items to return; must be non-negative.
filter_already_liked_items (bool, default True) – Skip items with a nonzero entry in the user’s row.

Returns:

ids (ndarray of shape (<= N,), int32)
scores (ndarray of shape (<= N,), float32)

Raises:

ValueError – If N is negative or user_items has no matching row.

similar_items(itemid, N=10)[source]#

Return the N items most similar to one item.

Similarity is the cosine of the item factors. The item itself is included, as implicit returns it.

Parameters:

itemid (int)
N (int, default 10) – Must be non-negative.

Returns:

ids (ndarray of shape (<= N,), int32)
scores (ndarray of shape (<= N,), float32)