# Copyright 2018 The Cornac Authors. All Rights Reserved.
#
# 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.
# ============================================================================
import multiprocessing
import numpy as np
from ..recommender import Recommender
from ..recommender import ANNMixin, MEASURE_DOT
from ...exception import ScoreException
from ...utils import fast_dot
from ...utils import get_rng
from ...utils.init_utils import normal, zeros
[docs]
class MF(Recommender, ANNMixin):
"""Matrix Factorization.
Parameters
----------
k: int, optional, default: 10
The dimension of the latent factors.
backend: str, optional, default: 'cpu'
Backend used for model training: cpu, pytorch
optimizer: str, optional, default: 'sgd'
Specify an optimizer: adagrad, adam, rmsprop, sgd. (ineffective if using CPU backend)
max_iter: int, optional, default: 100
Maximum number of iterations or the number of epochs for training.
learning_rate: float, optional, default: 0.01
The learning rate.
batch_size: int, optional, default: 256
Batch size (ineffective if using CPU backend).
lambda_reg: float, optional, default: 0.001
The lambda value used for regularization.
dropout: float, optional, default: 0.0
The dropout rate of embedding. (ineffective if using CPU backend)
use_bias: boolean, optional, default: True
When True, user, item, and global biases are used.
early_stop: boolean, optional, default: False
When True, delta loss will be checked after each iteration to stop learning earlier.
num_threads: int, optional, default: 0
Number of parallel threads for training. If num_threads=0, all CPU cores will be utilized.
If seed is not None, num_threads=1 to remove randomness from parallelization.
(Only effective if using CPU backend).
trainable: boolean, optional, default: True
When False, the model will not be re-trained, and input of pre-trained parameters are required.
verbose: boolean, optional, default: True
When True, running logs are displayed.
init_params: dictionary, optional, default: None
Initial parameters, e.g., init_params = {'U': user_factors, 'V': item_factors,
'Bu': user_biases, 'Bi': item_biases}
seed: int, optional, default: None
Random seed for weight initialization.
If specified, training will take longer because of single-thread (no parallelization).
References
----------
* Koren, Y., Bell, R., & Volinsky, C. Matrix factorization techniques for recommender systems. \
In Computer, (8), 30-37. 2009.
"""
def __init__(
self,
name="MF",
k=10,
backend="cpu",
optimizer="sgd",
max_iter=20,
learning_rate=0.01,
batch_size=256,
lambda_reg=0.02,
dropout=0.0,
use_bias=True,
early_stop=False,
num_threads=0,
trainable=True,
verbose=False,
init_params=None,
seed=None,
):
super().__init__(name=name, trainable=trainable, verbose=verbose)
self.k = k
self.backend = backend
self.optimizer = optimizer
self.max_iter = max_iter
self.learning_rate = learning_rate
self.batch_size = batch_size
self.lambda_reg = lambda_reg
self.dropout = dropout
self.use_bias = use_bias
self.early_stop = early_stop
self.seed = seed
if seed is not None:
self.num_threads = 1
elif num_threads > 0 and num_threads < multiprocessing.cpu_count():
self.num_threads = num_threads
else:
self.num_threads = multiprocessing.cpu_count()
# Init params if provided
self.init_params = {} if init_params is None else init_params
self.u_factors = self.init_params.get("U", None)
self.i_factors = self.init_params.get("V", None)
self.u_biases = self.init_params.get("Bu", None)
self.i_biases = self.init_params.get("Bi", None)
def _init(self):
rng = get_rng(self.seed)
if self.u_factors is None:
self.u_factors = normal(
[self.num_users, self.k], std=0.01, random_state=rng
)
if self.i_factors is None:
self.i_factors = normal(
[self.num_items, self.k], std=0.01, random_state=rng
)
self.u_biases = (
zeros(self.num_users) if self.u_biases is None else self.u_biases
)
self.i_biases = (
zeros(self.num_items) if self.i_biases is None else self.i_biases
)
self.global_mean = self.global_mean if self.use_bias else 0.0
[docs]
def fit(self, train_set, val_set=None):
"""Fit the model to observations.
Parameters
----------
train_set: :obj:`cornac.data.Dataset`, required
User-Item preference data as well as additional modalities.
val_set: :obj:`cornac.data.Dataset`, optional, default: None
User-Item preference data for model selection purposes (e.g., early stopping).
Returns
-------
self : object
"""
Recommender.fit(self, train_set, val_set)
self._init()
if self.trainable:
if self.backend == "cpu":
self._fit_cpu(train_set, val_set)
elif self.backend == "pytorch":
self._fit_pt(train_set, val_set)
else:
raise ValueError(f"{self.backend} is not supported")
return self
#################
## CPU backend ##
#################
def _fit_cpu(self, train_set, val_set):
from cornac.models.mf import backend_cpu
(rid, cid, val) = train_set.uir_tuple
backend_cpu.fit_sgd(
rid,
cid,
val.astype(np.float32),
self.u_factors,
self.i_factors,
self.u_biases,
self.i_biases,
self.num_users,
self.num_items,
self.learning_rate,
self.lambda_reg,
self.global_mean,
self.max_iter,
self.num_threads,
self.use_bias,
self.early_stop,
self.verbose,
)
#####################
## PyTorch backend ##
#####################
def _fit_pt(self, train_set, val_set):
import torch
from .backend_pt import MF, learn
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.device = device
if self.seed is not None:
torch.manual_seed(self.seed)
np.random.seed(self.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(self.seed)
model = MF(
self.u_factors,
self.i_factors,
self.u_biases.reshape(-1, 1),
self.i_biases.reshape(-1, 1),
self.use_bias,
self.global_mean,
self.dropout,
)
learn(
model=model,
train_set=train_set,
n_epochs=self.max_iter,
batch_size=self.batch_size,
learning_rate=self.learning_rate,
reg=self.lambda_reg,
optimizer=self.optimizer,
device=device,
)
self.u_factors = model.u_factors.weight.detach().cpu().numpy()
self.i_factors = model.i_factors.weight.detach().cpu().numpy()
if self.use_bias:
self.u_biases = model.u_biases.weight.detach().cpu().squeeze().numpy()
self.i_biases = model.i_biases.weight.detach().cpu().squeeze().numpy()
[docs]
def score(self, user_idx, item_idx=None):
"""Predict the scores/ratings of a user for an item.
Parameters
----------
user_idx: int, required
The index of the user for whom to perform score prediction.
item_idx: int, optional, default: None
The index of the item for which to perform score prediction.
If None, scores for all known items will be returned.
Returns
-------
res : A scalar or a Numpy array
Relative scores that the user gives to the item or to all known items
"""
if item_idx is not None and self.is_unknown_item(item_idx):
raise ScoreException("Can't make score prediction for item %d" % item_idx)
if item_idx is None:
known_item_scores = self.global_mean + self.i_biases
if self.knows_user(user_idx):
known_item_scores += self.u_biases[user_idx]
fast_dot(self.u_factors[user_idx], self.i_factors, known_item_scores)
return known_item_scores
else:
item_score = self.global_mean + self.i_biases[item_idx]
if self.knows_user(user_idx):
item_score += self.u_biases[user_idx]
item_score += self.u_factors[user_idx].dot(self.i_factors[item_idx])
return item_score
[docs]
def get_vector_measure(self):
"""Getting a valid choice of vector measurement in ANNMixin._measures.
Returns
-------
measure: MEASURE_DOT
Dot product aka. inner product
"""
return MEASURE_DOT
[docs]
def get_user_vectors(self):
"""Getting a matrix of user vectors serving as query for ANN search.
Returns
-------
out: numpy.array
Matrix of user vectors for all users available in the model.
"""
user_vectors = self.u_factors
if self.use_bias:
user_vectors = np.concatenate(
(user_vectors, np.ones([user_vectors.shape[0], 1])), axis=1
)
return user_vectors
[docs]
def get_item_vectors(self):
"""Getting a matrix of item vectors used for building the index for ANN search.
Returns
-------
out: numpy.array
Matrix of item vectors for all items available in the model.
"""
item_vectors = self.i_factors
if self.use_bias:
item_vectors = np.concatenate(
(item_vectors, self.i_biases.reshape((-1, 1))), axis=1
)
return item_vectors
