# Source code for cornac.models.pcrl.recom_pcrl

```
# 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
import scipy.sparse as sp
from ..recommender import Recommender
# Recommender class for Probabilistic Collaborative Representation Learning (PCRL)
[docs]class PCRL(Recommender):
"""Probabilistic Collaborative Representation Learning.
Parameters
----------
k: int, optional, default: 100
The dimension of the latent factors.
z_dims: Numpy 1d array, optional, default: [300]
The dimensions of the hidden intermdiate layers 'z' in the order \
[dim(z_L), ...,dim(z_1)], please refer to Figure 1 in the orginal paper for more details.
max_iter: int, optional, default: 300
Maximum number of iterations (number of epochs) for variational PCRL.
batch_size: int, optional, default: 300
The batch size for SGD.
learning_rate: float, optional, default: 0.001
The learning rate for SGD.
aux_info: see "cornac/examples/pcrl_example.py" in the GitHub repo for an example of how to use \
cornac's graph modality provide item auxiliary data (e.g., context, text, etc.) for PCRL.
name: string, optional, default: 'PCRL'
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 (Theta, Beta and Xi are not None).
w_determinist: boolean, optional, default: True
When True, determinist wheights "W" are used for the generator network, \
otherwise "W" is stochastic as in the original paper.
init_params: dictionary, optional, default: {'G_s':None, 'G_r':None, 'L_s':None, 'L_r':None}
List of initial parameters, e.g., init_params = {'G_s':G_s, 'G_r':G_r, 'L_s':L_s, 'L_r':L_r}, \
where G_s and G_r are of type csc_matrix or np.array with the same shape as Theta, see below). \
They represent respectively the "shape" and "rate" parameters of Gamma distribution over \
Theta. It is the same for L_s, L_r and Beta.
Theta: csc_matrix, shape (n_users,k)
The expected user latent factors.
Beta: csc_matrix, shape (n_items,k)
The expected item latent factors.
References
----------
* Salah, Aghiles, and Hady W. Lauw. Probabilistic Collaborative Representation Learning for Personalized Item Recommendation. \
In UAI 2018.
"""
def __init__(self, k=100, z_dims=[300], max_iter=300, batch_size=300, learning_rate=0.001, name="pcrl",
trainable=True,
verbose=False, w_determinist=True, init_params={'G_s': None, 'G_r': None, 'L_s': None, 'L_r': None}):
Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
self.k = k
self.z_dims = z_dims # the dimension of the second hidden layer (we consider a 2-layers PCRL)
self.max_iter = max_iter
self.batch_size = batch_size
self.learning_rate = learning_rate
self.init_params = init_params
self.w_determinist = w_determinist
# fit the recommender model to the traning data
[docs] def fit(self, train_set):
"""Fit the model to observations.
Parameters
----------
train_set: object of type TrainSet, required
An object contraining the user-item preference in csr scipy sparse format,\
as well as some useful attributes such as mappings to the original user/item ids.\
Please refer to the class TrainSet in the "data" module for details.
"""
from .pcrl import PCRL_
Recommender.fit(self, train_set)
X = sp.csc_matrix(self.train_set.matrix)
if self.trainable:
# intanciate pcrl
train_aux_info = train_set.item_graph.matrix[:self.train_set.num_items, :self.train_set.num_items]
pcrl_ = PCRL_(cf_data=X, aux_data=train_aux_info, k=self.k, z_dims=self.z_dims, n_epoch=self.max_iter,
batch_size=self.batch_size, learning_rate=self.learning_rate, B=1,
w_determinist=self.w_determinist, init_params=self.init_params)
pcrl_.learn()
self.Theta = np.array(pcrl_.Gs) / np.array(pcrl_.Gr)
self.Beta = np.array(pcrl_.Ls) / np.array(pcrl_.Lr)
elif self.verbose:
print('%s is trained already (trainable = False)' % (self.name))
[docs] def score(self, user_id, item_id=None):
"""Predict the scores/ratings of a user for a list of items.
Parameters
----------
user_id: int, required
The index of the user for whom to perform score prediction.
item_id: int, optional, default: None
The index of the item for that 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_id is None:
user_pred = self.Beta * self.Theta[user_id, :].T
else:
user_pred = self.Beta[item_id, :] * self.Theta[user_id, :].T
# transform user_pred to a flatten array
user_pred = np.array(user_pred, dtype='float64').flatten()
return user_pred
```