Support ANN search for existing models (#549)

cornac/models/amr/recom_amr.py

@@ -17,13 +17,14 @@
 from tqdm.auto import tqdm
 
 from ..recommender import Recommender
+from ..recommender import ANNMixin, MEASURE_DOT
 from ...exception import CornacException
 from ...utils import fast_dot
 from ...utils import get_rng
 from ...utils.init_utils import xavier_uniform
 
 
-class AMR(Recommender):
+class AMR(Recommender, ANNMixin):
     """Adversarial Training Towards Robust Multimedia Recommender System.
 
     Parameters
@@ -286,3 +287,35 @@ def score(self, user_idx, item_idx=None):
             item_score = np.dot(self.gamma_item[item_idx], self.gamma_user[user_idx])
             item_score += np.dot(self.theta_item[item_idx], self.gamma_user[user_idx])
             return item_score
+
+    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
+
+    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.gamma_user, self.gamma_user), axis=1)
+        return user_vectors
+
+    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.gamma_item, self.theta_item), axis=1)
+        return item_vectors

cornac/models/bivaecf/recom_bivaecf.py

@@ -16,11 +16,12 @@
 import numpy as np
 
 from ..recommender import Recommender
+from ..recommender import ANNMixin, MEASURE_DOT
 from ...utils.common import scale
 from ...exception import ScoreException
 
 
-class BiVAECF(Recommender):
+class BiVAECF(Recommender, ANNMixin):
     """Bilateral Variational AutoEncoder for Collaborative Filtering.
 
     Parameters
@@ -160,7 +161,7 @@ def fit(self, train_set, val_set=None):
                 torch.manual_seed(self.seed)
                 torch.cuda.manual_seed(self.seed)
 
-            if not hasattr(self, "bivaecf"):
+            if not hasattr(self, "bivae"):
                 num_items = train_set.matrix.shape[1]
                 num_users = train_set.matrix.shape[0]
                 self.bivae = BiVAE(
@@ -207,26 +208,50 @@ def score(self, user_idx, item_idx=None):
             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:
-            if not self.knows_user(user_idx):
-                raise ScoreException(
-                    "Can't make score prediction for (user_id=%d)" % user_idx
-                )
             theta_u = self.bivae.mu_theta[user_idx].view(1, -1)
             beta = self.bivae.mu_beta
-            known_item_scores = (
-                self.bivae.decode_user(theta_u, beta).cpu().numpy().ravel()
-            )
-            return known_item_scores
+            return self.bivae.decode_user(theta_u, beta).cpu().numpy().ravel()
         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)
-                )
             theta_u = self.bivae.mu_theta[user_idx].view(1, -1)
             beta_i = self.bivae.mu_beta[item_idx].view(1, -1)
             pred = self.bivae.decode_user(theta_u, beta_i).cpu().numpy().ravel()
-            pred = scale(pred, self.min_rating, self.max_rating, 0.0, 1.0)
-            return pred
+            return scale(pred, self.min_rating, self.max_rating, 0.0, 1.0)
+
+    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
+
+    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 = self.bivae.mu_theta.detach().cpu().numpy()
+        return user_vectors
+
+    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 = self.bivae.mu_beta.detach().cpu().numpy()
+        return item_vectors

cornac/models/bpr/recom_bpr.pyx

@@ -29,6 +29,7 @@ cimport numpy as np
 from tqdm.auto import trange
 
 from ..recommender import Recommender
+from ..recommender import ANNMixin, MEASURE_DOT
 from ...exception import ScoreException
 from ...utils import get_rng
 from ...utils import fast_dot
@@ -60,7 +61,7 @@ cdef class RNGVector(object):
 
 
 
-class BPR(Recommender):
+class BPR(Recommender, ANNMixin):
     """Bayesian Personalized Ranking.
 
     Parameters
@@ -292,4 +293,40 @@ class BPR(Recommender):
         else:
             item_score = self.i_biases[item_idx]
             item_score += np.dot(self.u_factors[user_idx], self.i_factors[item_idx])
-            return item_score
+            return item_score
+
+    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
+
+    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_factors, np.ones([self.u_factors.shape[0], 1])), axis=1
+        )
+        return user_vectors
+
+    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.i_factors, self.i_biases.reshape((-1, 1))), axis=1
+        )
+        return item_vectors

cornac/models/c2pf/recom_c2pf.py

@@ -18,10 +18,11 @@
 
 from cornac.models.c2pf import c2pf
 from ..recommender import Recommender
+from ..recommender import ANNMixin, MEASURE_DOT
 
 
 # Recommender class for Collaborative Context Poisson Factorization (C2PF)
-class C2PF(Recommender):
+class C2PF(Recommender, ANNMixin):
     """Collaborative Context Poisson Factorization.
 
     Parameters
@@ -295,3 +296,41 @@ def score(self, user_idx, item_idx=None):
         # transform user_pred to a flatten array,
         user_pred = np.array(user_pred, dtype="float64").flatten()
         return user_pred
+
+    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
+
+    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.
+        """
+        if self.variant == "rc2pf":
+            user_vectors = np.concatenate((self.Theta, self.Theta), axis=1)
+        else:
+            user_vectors = self.Theta
+        return user_vectors
+
+    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.
+        """
+        if self.variant == "rc2pf":
+            item_vectors = np.concatenate((self.Beta, self.Xi), axis=1)
+        else:
+            item_vectors = self.Beta
+        return item_vectors

cornac/models/cdl/recom_cdl.py

@@ -17,12 +17,13 @@
 from tqdm.auto import trange
 
 from ..recommender import Recommender
+from ..recommender import ANNMixin, MEASURE_DOT
 from ...exception import ScoreException
 from ...utils import get_rng
 from ...utils.init_utils import xavier_uniform
 
 
-class CDL(Recommender):
+class CDL(Recommender, ANNMixin):
     """Collaborative Deep Learning.
 
     Parameters
@@ -280,18 +281,43 @@ def score(self, user_idx, item_idx=None):
         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:
-            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 not 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, :])
-            return user_pred
+            return self.V.dot(self.U[user_idx, :])
+
+        return self.V[item_idx, :].dot(self.U[user_idx, :])
+
+    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
+
+    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
+
+    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