# 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 MCF(Recommender, ANNMixin):
"""Matrix Co-Factorization.
Parameters
----------
k: int, optional, default: 5
The dimension of the latent factors.
max_iter: int, optional, default: 100
Maximum number of iterations or 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.
lamda: float, optional, default: 0.001
The regularization parameter.
name: string, optional, default: 'MCF'
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 (U and V are not None).
item-affinity network: See "cornac/examples/mcf_office.py" for an example of how to use \
cornac's graph modality to load and provide the "item-affinity network" for MCF.
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, 'Z', Z}.
U: ndarray, shape (n_users, k)
User latent factors.
V: ndarray, shape (n_items, k)
Item latent factors.
Z: ndarray, shape (n_items, k)
The "Also-Viewed" item latent factors.
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,
k=5,
max_iter=100,
learning_rate=0.001,
gamma=0.9,
lamda=0.001,
name="MCF",
trainable=True,
verbose=False,
init_params=None,
seed=None,
):
Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
self.k = k
self.max_iter = max_iter
self.learning_rate = learning_rate
self.gamma = gamma
self.lamda = lamda
self.seed = seed
self.ll = np.full(max_iter, 0)
self.eps = 0.000000001
# Init params if provided
self.init_params = {} if init_params is None else init_params
self.U = self.init_params.get("U", None) # matrix of user factors
self.V = self.init_params.get("V", None) # matrix of item factors
self.Z = self.init_params.get("Z", None) # matrix of Also-Viewed item factors
[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:
# user-item interactions
(rat_uid, rat_iid, rat_val) = train_set.uir_tuple
# item-item affinity network
train_item_indices = set(train_set.uir_tuple[1])
(net_iid, net_jid, net_val) = train_set.item_graph.get_train_triplet(
train_item_indices, train_item_indices
)
if [self.min_rating, self.max_rating] != [0, 1]:
if self.min_rating == self.max_rating:
rat_val = scale(rat_val, 0.0, 1.0, 0.0, self.max_rating)
else:
rat_val = scale(rat_val, 0.0, 1.0, self.min_rating, self.max_rating)
if [min(net_val), max(net_val)] != [0, 1]:
if min(net_val) == max(net_val):
net_val = scale(net_val, 0.0, 1.0, 0.0, max(net_val))
else:
net_val = scale(net_val, 0.0, 1.0, min(net_val), max(net_val))
rat_val = np.array(rat_val, dtype="float32")
rat_uid = np.array(rat_uid, dtype="int32")
rat_iid = np.array(rat_iid, dtype="int32")
net_val = np.array(net_val, dtype="float32")
net_iid = np.array(net_iid, dtype="int32")
net_jid = np.array(net_jid, dtype="int32")
if self.verbose:
print("Learning...")
from cornac.models.mcf import mcf
res = mcf.mcf(
rat_uid,
rat_iid,
rat_val,
net_iid,
net_jid,
net_val,
k=self.k,
n_users=train_set.num_users,
n_items=train_set.num_items,
n_ratings=len(rat_val),
n_edges=len(net_val),
n_epochs=self.max_iter,
lamda=self.lamda,
learning_rate=self.learning_rate,
gamma=self.gamma,
init_params={"U": self.U, "V": self.V, "Z": self.Z},
verbose=self.verbose,
seed=self.seed,
)
self.U = np.asarray(res["U"])
self.V = np.asarray(res["V"])
self.Z = np.asarray(res["Z"])
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 item_idx is None:
if not self.knows_user(user_idx):
raise ScoreException(
"Can't make score prediction for (user_id=%d)" % user_idx
)
known_item_scores = self.V.dot(self.U[user_idx, :])
return known_item_scores
else:
if not (self.knows_user(user_idx) and self.knows_item(item_idx)):
raise ScoreException(
"Can't make score prediction for (user_id=%d, item_id=%d)"
% (user_idx, item_idx)
)
user_pred = self.V[item_idx, :].dot(self.U[user_idx, :])
user_pred = sigmoid(user_pred)
if self.min_rating == self.max_rating:
user_pred = scale(user_pred, 0.0, self.max_rating, 0.0, 1.0)
else:
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.
"""
return self.U
[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.
"""
return self.V