add NCF_PyTorch models

.gitignore

@@ -1,5 +1,6 @@
 tests/vocab.pkl
 .idea/
+.vscode/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

cornac/models/__init__.py

@@ -52,6 +52,7 @@
 from .ncf import GMF
 from .ncf import MLP
 from .ncf import NeuMF
+from .ncf import GMF_PyTorch, MLP_PyTorch, NeuMF_PyTorch
 from .ngcf import NGCF
 from .nmf import NMF
 from .online_ibpr import OnlineIBPR
@@ -74,4 +75,3 @@
         "FM model is only supported on Linux.\n"
         + "Windows executable can be found at http://www.libfm.org."
     )
-

cornac/models/ncf/__init__.py

@@ -16,3 +16,6 @@
 from .recom_gmf import GMF
 from .recom_mlp import MLP
 from .recom_neumf import NeuMF
+from .pytorch_gmf import GMF_PyTorch
+from .pytorch_mlp import MLP_PyTorch
+from .pytorch_neumf import NeuMF_PyTorch

cornac/models/ncf/pytorch_gmf.py

@@ -0,0 +1,267 @@
+# 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 torch
+import torch.nn as nn
+from tqdm.auto import trange
+
+from .pytorch_ncf_base import NCFBase_PyTorch
+from ...exception import ScoreException
+
+
+class GMF_PyTorch(NCFBase_PyTorch):
+    """Generalized Matrix Factorization.
+
+    Parameters
+    ----------
+    num_factors: int, optional, default: 8
+        Embedding size of MF model.
+
+    regs: float, optional, default: 0.
+        Regularization for user and item embeddings.
+
+    num_epochs: int, optional, default: 20
+        Number of epochs.
+
+    batch_size: int, optional, default: 256
+        Batch size.
+
+    num_neg: int, optional, default: 4
+        Number of negative instances to pair with a positive instance.
+
+    lr: float, optional, default: 0.001
+        Learning rate.
+
+    learner: str, optional, default: 'adam'
+        Specify an optimizer: adagrad, adam, rmsprop, sgd
+
+    early_stopping: {min_delta: float, patience: int}, optional, default: None
+        If `None`, no early stopping. Meaning of the arguments: 
+        
+        - `min_delta`: the minimum increase in monitored value on validation set to be considered as improvement, \
+           i.e. an increment of less than min_delta will count as no improvement.
+        
+        - `patience`: number of epochs with no improvement after which training should be stopped.
+
+    name: string, optional, default: 'GMF'
+        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.
+
+    verbose: boolean, optional, default: False
+        When True, some running logs are displayed.
+
+    seed: int, optional, default: None
+        Random seed for parameters initialization.
+
+    References
+    ----------
+    * He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. \
+    In Proceedings of the 26th international conference on world wide web (pp. 173-182).
+    """
+
+    def __init__(
+        self,
+        name="GMF-PyTorch",
+        num_factors=8,
+        num_epochs=20,
+        batch_size=256,
+        num_neg=4,
+        lr=1e-3,
+        reg=0.0,
+        learner="adam",
+        early_stopping=None,
+        trainable=True,
+        verbose=True,
+        seed=None,
+        use_pretrain: bool = False,
+        use_NeuMF: bool = False,
+        pretrained_GMF=None,
+        sinkhorn=False,
+        alpha=1,
+        df1=None,
+        df2=None,
+        args=None,
+    ):
+        super().__init__(
+            name=name,
+            num_factors=num_factors,
+            trainable=trainable,
+            verbose=verbose,
+            num_epochs=num_epochs,
+            batch_size=batch_size,
+            num_neg=num_neg,
+            lr=lr,
+            reg=reg,
+            learner=learner,
+            early_stopping=early_stopping,
+            seed=seed,
+            use_pretrain=use_pretrain,
+            use_NeuMF=use_NeuMF,
+            pretrained_GMF=pretrained_GMF,
+        )
+
+        self.sinkhorn = sinkhorn
+        self.alpha = alpha
+        self.df1 = df1
+        self.df2 = df2
+        self.args = args
+
+    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
+        """
+        super().fit(train_set, val_set)
+
+        if self.trainable is False:
+            return self
+
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.device = device
+        if self.seed is not None:
+            torch.manual_seed(self.seed)
+            np.random.seed(self.seed)
+            if torch.cuda.is_available():
+                torch.cuda.manual_seed(self.seed)
+
+        from .pytorch_ncf_base import GMF_torch as GMF
+
+        self.model = GMF(
+            self.num_users,
+            self.num_items,
+            self.num_factors,
+            self.use_pretrain,
+            self.use_NeuMF,
+            self.pretrained_GMF,
+        ).to(self.device)
+
+        criteria = nn.MSELoss(reduction="sum")
+        optimizer = self.learner(
+            self.model.parameters(),
+            lr=self.lr,
+            weight_decay=self.reg,
+        )
+
+        loop = trange(self.num_epochs, disable=not self.verbose)
+        for _ in loop:
+            count = 0
+            sum_loss = 0
+            for batch_id, (batch_users, batch_items, batch_ratings) in enumerate(
+                self.train_set.uir_iter(
+                    self.batch_size, shuffle=True, binary=True, num_zeros=self.num_neg
+                )
+            ):
+                batch_users = torch.from_numpy(batch_users).to(self.device)
+                batch_items = torch.from_numpy(batch_items).to(self.device)
+                batch_ratings = torch.tensor(batch_ratings, dtype=torch.float).to(
+                    self.device
+                )
+
+                optimizer.zero_grad()
+                outputs = self.model(batch_users, batch_items)
+                loss = criteria(outputs, batch_ratings)
+                loss.backward()
+                optimizer.step()
+
+                count += len(batch_users)
+                sum_loss += loss.data.item()
+
+                if batch_id % 10 == 0:
+                    loop.set_postfix(loss=(sum_loss / count))
+
+            if self.sinkhorn:
+                df1 = self.df1
+                df2 = self.df2
+                args = self.args
+                assert df1 is not None and df2 is not None
+                import geomloss
+
+                uid_df1 = df1["user_id"].unique()
+                uid_df2 = df2["user_id"].unique()
+                uidx_1 = torch.tensor([train_set.uid_map[key] for key in uid_df1]).to(
+                    device
+                )
+                uidx_2 = torch.tensor([train_set.uid_map[key] for key in uid_df2]).to(
+                    device
+                )
+                sinkhorn_loss = geomloss.SamplesLoss(
+                    loss="sinkhorn",
+                    p=1,
+                    blur=args.epsilon,
+                    scaling=args.scaling,
+                )
+                l_s = self.alpha * sinkhorn_loss(
+                    self.model.u_factors(uidx_1), self.model.u_factors(uidx_2)
+                )
+                optimizer.zero_grad()
+                l_s.backward()
+                optimizer.step()
+
+    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 self.train_set.is_unk_user(user_idx):
+                raise ScoreException(
+                    "Can't make score prediction for (user_id=%d)" % user_idx
+                )
+
+            item_ids = torch.from_numpy(np.arange(self.train_set.num_items)).to(
+                self.device
+            )
+            user_ids = torch.tensor(user_idx).unsqueeze(0).to(self.device)
+
+            known_item_scores = self.model.predict(user_ids, item_ids).squeeze()
+            return known_item_scores.cpu().numpy()
+        else:
+            if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(
+                item_idx
+            ):
+                raise ScoreException(
+                    "Can't make score prediction for (user_id=%d, item_id=%d)"
+                    % (user_idx, item_idx)
+                )
+
+            user_pred = self.model.predict(user_ids, item_ids).squeeze()
+            return user_pred.cpu().numpy()