# Copyright 2022 MTS (Mobile Telesystems)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""SVD Model."""
import typing as tp
import numpy as np
from scipy.sparse.linalg import svds
from rectools.dataset import Dataset
from rectools.exceptions import NotFittedError
from rectools.models.rank import Distance
from rectools.models.vector import Factors, VectorModel
[docs]class PureSVDModel(VectorModel):
"""
PureSVD matrix factorization model.
See https://dl.acm.org/doi/10.1145/1864708.1864721
Parameters
----------
factors : int, default ``10``
The number of latent factors to compute.
verbose : int, default ``0``
Degree of verbose output. If ``0``, no output will be provided.
"""
recommends_for_warm = False
recommends_for_cold = False
u2i_dist = Distance.DOT
i2i_dist = Distance.COSINE
def __init__(self, factors: int = 10, verbose: int = 0):
super().__init__(verbose=verbose)
self.factors = factors
self.user_factors: np.ndarray
self.item_factors: np.ndarray
def _fit(self, dataset: Dataset) -> None: # type: ignore
ui_csr = dataset.get_user_item_matrix(include_weights=True)
u, sigma, vt = svds(ui_csr, k=self.factors)
self.user_factors = u
self.item_factors = (np.diag(sigma) @ vt).T
def _get_users_factors(self, dataset: Dataset) -> Factors:
return Factors(self.user_factors)
def _get_items_factors(self, dataset: Dataset) -> Factors:
return Factors(self.item_factors)
[docs] def get_vectors(self) -> tp.Tuple[np.ndarray, np.ndarray]:
"""
Return user and item vector representations from fitted model.
Returns
-------
(np.ndarray, np.ndarray)
User and item embeddings.
Shapes are (n_users, n_factors) and (n_items, n_factors).
"""
if not self.is_fitted:
raise NotFittedError(self.__class__.__name__)
return self.user_factors, self.item_factors