# 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_L2
from ...exception import ScoreException
[docs]
class COE(Recommender, ANNMixin):
"""Collaborative Ordinal Embedding.
Parameters
----------
k: int, optional, default: 20
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: 0.05
The learning rate for SGD.
lamda: float, optional, default: 0.001
The regularization parameter.
batch_size: int, optional, default: 100
The batch size for SGD.
name: string, optional, default: 'IBRP'
The name of the recommender model.
trainable: boolean, optional, default: True
When False, the model is not trained and Cornac assumes that the model already \
pre-trained (U and V are not None).
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}.
U: ndarray, shape (n_users, k)
The user latent factors.
V: ndarray, shape (n_items, k)
The item latent factors.
References
----------
* Le, D. D., & Lauw, H. W. (2016, June). Euclidean co-embedding of ordinal data for multi-type visualization.\
In Proceedings of the 2016 SIAM International Conference on Data Mining (pp. 396-404). Society for Industrial and Applied Mathematics.
"""
def __init__(
self,
k=20,
max_iter=100,
learning_rate=0.05,
lamda=0.001,
batch_size=1000,
name="coe",
trainable=True,
verbose=False,
init_params=None,
):
Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
self.k = k
self.max_iter = max_iter
self.name = name
self.learning_rate = learning_rate
self.lamda = lamda
self.batch_size = batch_size
# 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
[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:
from .coe import coe
if self.verbose:
print("Learning...")
res = coe(
train_set.matrix,
k=self.k,
n_epochs=self.max_iter,
lamda=self.lamda,
learning_rate=self.learning_rate,
batch_size=self.batch_size,
init_params={"U": self.U, "V": self.V},
)
self.U = np.asarray(res["U"])
self.V = np.asarray(res["V"])
if self.verbose:
print("Learning completed")
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:
return np.sum((self.V - self.U[user_idx, :]) ** 2, axis=-1) ** (1.0 / 2)
return np.sum((self.V[item_idx, :] - self.U[user_idx, :]) ** 2, axis=-1) ** (
1.0 / 2
)
[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_L2
[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
