# 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 numpy as np
from ..recommender import Recommender
from ..recommender import ANNMixin, MEASURE_DOT
from ...utils.common import sigmoid
from ...utils.common import scale
from ...exception import ScoreException
[docs]
class VMF(Recommender, ANNMixin):
"""Visual Matrix Factorization.
Parameters
----------
k: int, optional, default: 10
The dimension of the user and item factors.
d: int, optional, default: 10
The dimension of the user visual factors.
n_epochs: int, optional, default: 100
The number of epochs for SGD.
learning_rate: float, optional, default: 0.001
The learning rate for SGD_RMSProp.
gamma: float, optional, default: 0.9
The weight for previous/current gradient in RMSProp.
lambda_u: float, optional, default: 0.001
The regularization parameter for user factors.
lambda_v: float, optional, default: 0.001
The regularization parameter for item factors.
lambda_p: float, optional, default: 1.0
The regularization parameter for user visual factors.
lambda_e: float, optional, default: 10.
The regularization parameter for the kernel embedding matrix
lambda_u: float, optional, default: 0.001
The regularization parameter for user factors.
name: string, optional, default: 'VMF'
The name of the recommender model.
trainable: boolean, optional, default: True
When False, the model is not trained and Cornac assumes that the model is already \
pre-trained (The parameters of the model U, V, P, E are not None).
visual_features: See "cornac/examples/vmf_example.py" for an example of how to use \
cornac's visual modality to load and provide the "item visual features" for VMF.
verbose: boolean, optional, default: False
When True, some running logs are displayed.
init_params: dictionary, optional, default: None
List of initial parameters, e.g., init_params = {'U':U, 'V':V, 'P': P, 'E': E}.
U: numpy array of shape (n_users,k), user latent factors.
V: numpy array of shape (n_items,k), item latent factors.
P: numpy array of shape (n_users,d), user visual latent factors.
E: numpy array of shape (d,c), embedding kernel matrix.
seed: int, optional, default: None
Random seed for parameters initialization.
References
----------
* Park, Chanyoung, Donghyun Kim, Jinoh Oh, and Hwanjo Yu. "Do Also-Viewed Products Help User Rating Prediction?."\
In Proceedings of WWW, pp. 1113-1122. 2017.
"""
def __init__(
self,
name="VMF",
k=10,
d=10,
n_epochs=100,
batch_size=100,
learning_rate=0.001,
gamma=0.9,
lambda_u=0.001,
lambda_v=0.001,
lambda_p=1.0,
lambda_e=10.0,
trainable=True,
verbose=False,
use_gpu=False,
init_params=None,
seed=None,
):
Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
self.k = k
self.d = d
self.batch_size = batch_size
self.n_epochs = n_epochs
self.learning_rate = learning_rate
self.gamma = gamma
self.lambda_u = lambda_u
self.lambda_v = lambda_v
self.lambda_p = lambda_p
self.lambda_e = lambda_e
self.use_gpu = use_gpu
self.loss = np.full(n_epochs, 0)
self.eps = 0.000000001
self.seed = seed
# Init params if provided
self.init_params = {} if init_params is None else init_params
self.U = self.init_params.get("U", None) # user factors
self.V = self.init_params.get("V", None) # item factors
self.P = self.init_params.get("P", None) # user visual factors
self.E = self.init_params.get("E", None) # Kernel embedding matrix
[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)
if self.trainable:
# Item visual cnn-features
item_features = train_set.item_image.features[: train_set.num_items]
if self.verbose:
print("Learning...")
from .vmf import vmf
res = vmf(
train_set,
item_features,
k=self.k,
d=self.d,
n_epochs=self.n_epochs,
batch_size=self.batch_size,
lambda_u=self.lambda_u,
lambda_v=self.lambda_v,
lambda_p=self.lambda_p,
lambda_e=self.lambda_e,
learning_rate=self.learning_rate,
gamma=self.gamma,
init_params={"U": self.U, "V": self.V, "P": self.P, "E": self.E},
use_gpu=self.use_gpu,
verbose=self.verbose,
seed=self.seed,
)
self.U = res["U"]
self.V = res["V"]
self.P = res["P"]
self.E = res["E"]
self.Q = res["Q"]
if self.verbose:
print("Learning completed")
elif self.verbose:
print("%s is trained already (trainable = False)" % (self.name))
return self
[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 self.is_unknown_user(user_idx):
raise ScoreException("Can't make score prediction for user %d" % user_idx)
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.V.dot(self.U[user_idx, :]) + self.Q.dot(
self.P[user_idx, :]
)
return known_item_scores
else:
user_pred = self.V[item_idx, :].dot(self.U[user_idx, :]) + self.Q[
item_idx, :
].dot(self.P[user_idx, :])
user_pred = sigmoid(user_pred)
user_pred = scale(user_pred, self.min_rating, self.max_rating, 0.0, 1.0)
return user_pred
[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 = np.concatenate((self.U, self.P), 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 = np.concatenate((self.V, self.Q), axis=1)
return item_vectors
