# 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 cornac.models.hpf import hpf
from ..recommender import Recommender
from ..recommender import ANNMixin, MEASURE_DOT
from ...exception import ScoreException
[docs]
class HPF(Recommender, ANNMixin):
"""Hierarchical Poisson Factorization.
Parameters
----------
k: int, optional, default: 5
The dimension of the latent factors.
max_iter: int, optional, default: 100
Maximum number of iterations.
name: string, optional, default: 'HPF'
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 (Theta and Beta are not None).
verbose: boolean, optional, default: False
When True, some running logs are displayed.
hierarchical: boolean, optional, default: True
When False, PF is used instead of HPF.
seed: int, optional, default: None
Random seed for parameters initialization.
init_params: dict, optional, default: None
Initial parameters of the model.
Theta: ndarray, shape (n_users, k)
The expected user latent factors.
Beta: ndarray, shape (n_items, k)
The expected item latent factors.
G_s: ndarray, shape (n_users, k)
This represents "shape" parameters of Gamma distribution over Theta.
G_r: ndarray, shape (n_users, k)
This represents "rate" parameters of Gamma distribution over Theta.
L_s: ndarray, shape (n_items, k)
This represents "shape" parameters of Gamma distribution over Beta.
L_r: ndarray, shape (n_items, k)
This represents "rate" parameters of Gamma distribution over Beta.
References
----------
* Gopalan, Prem, Jake M. Hofman, and David M. Blei. Scalable Recommendation with \
Hierarchical Poisson Factorization. In UAI, pp. 326-335. 2015.
"""
def __init__(
self,
k=5,
max_iter=100,
name="HPF",
trainable=True,
verbose=False,
hierarchical=True,
seed=None,
init_params=None,
):
Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
self.k = k
self.max_iter = max_iter
self.ll = np.full(max_iter, 0)
self.etp_r = np.full(max_iter, 0)
self.etp_c = np.full(max_iter, 0)
self.eps = 0.000000001
self.hierarchical = hierarchical
self.seed = seed
# Init params if provided
self.init_params = {} if init_params is None else init_params
self.Theta = self.init_params.get("Theta", None) # matrix of user factors
self.Beta = self.init_params.get("Beta", None) # matrix of item factors
self.Gs = self.init_params.get("G_s", None)
self.Gr = self.init_params.get("G_r", None)
self.Ls = self.init_params.get("L_s", None)
self.Lr = self.init_params.get("L_r", None)
[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:
# use pre-trained params if exists, otherwise from constructor
init_params = {
"G_s": self.Gs,
"G_r": self.Gr,
"L_s": self.Ls,
"L_r": self.Lr,
}
X = train_set.csc_matrix
# recover the striplet sparse format from csc sparse matrix X (needed to feed c++)
(rid, cid, val) = sp.find(X)
val = np.array(val, dtype="float32")
rid = np.array(rid, dtype="int32")
cid = np.array(cid, dtype="int32")
tX = np.concatenate(
(np.concatenate(([rid], [cid]), axis=0).T, val.reshape((len(val), 1))),
axis=1,
)
del rid, cid, val
if self.hierarchical:
res = hpf.hpf(
tX,
X.shape[0],
X.shape[1],
self.k,
self.max_iter,
self.seed,
init_params,
)
else:
res = hpf.pf(
tX,
X.shape[0],
X.shape[1],
self.k,
self.max_iter,
self.seed,
init_params,
)
self.Theta = np.asarray(res["Z"])
self.Beta = np.asarray(res["W"])
# overwrite init_params for future fine-tuning
self.Gs = np.asarray(res["G_s"])
self.Gr = np.asarray(res["G_r"])
self.Ls = np.asarray(res["L_s"])
self.Lr = np.asarray(res["L_r"])
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.Beta.dot(self.Theta[user_idx, :])
known_item_scores = np.array(known_item_scores, dtype="float64").flatten()
return known_item_scores
else:
user_pred = self.Beta[item_idx, :].dot(self.Theta[user_idx, :])
user_pred = np.array(user_pred, dtype="float64").flatten()[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.Theta
[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.Beta
