Develop

docs/history.rst

@@ -1,9 +1,9 @@
 =======
 History
 =======
-0.17.5 (2022-09-18)
+0.17.7 (2023-04-05)
 -------------------
-* Fix bug in TransH implementation
+* Adding additional pretrained models
 
 0.17.6 (2023-03-31)
 -------------------

docs/reference/utils.rst

@@ -42,6 +42,22 @@ TransE model
 
 .. autofunction:: torchkge.utils.pretrained_models.load_pretrained_transe
 
+RESCAL Model
+=============
+.. tabularcolumns:: p{3cm}p{3cm}p{3cm}p{3cm}
+
++-----------+-----------+-----------+----------+--------------------+
+| Model     | Dataset   | Dimension | Test MRR | Filtered Test MRR  |
++===========+===========+===========+==========+====================+
+| RESCAL    | FB15k237  | 200       | 0.180    | 0.305              |
++-----------+-----------+-----------+----------+--------------------+
+| RESCAL    | WN18RR    | 150       | 0.273    | 0.424              |
++-----------+-----------+-----------+----------+--------------------+
+| RESCAL    | Yago3-10  | 200       | 0.124    | 0.308              |
++-----------+-----------+-----------+----------+--------------------+
+
+.. autofunction:: torchkge.utils.pretrained_models.load_pretrained_rescal
+
 ComplEx Model
 =============
 .. tabularcolumns:: p{3cm}p{3cm}p{3cm}p{3cm}
@@ -55,6 +71,8 @@ ComplEx Model
 +-----------+-----------+-----------+----------+--------------------+
 | ComplEx   | WDV5      | 200       | 0.283    | 0.371              |
 +-----------+-----------+-----------+----------+--------------------+
+| ComplEx   | Yago3-10  | 200       | 0.164    | 0.421              |
++-----------+-----------+-----------+----------+--------------------+
 
 .. autofunction:: torchkge.utils.pretrained_models.load_pretrained_complex
 

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.17.6
+current_version = 0.17.7
 commit = True
 tag = True
 

setup.py

@@ -38,6 +38,6 @@
     setup_requires=setup_requirements,
     tests_require=test_requirements,
     test_suite='tests',
-    version='0.17.6',
+    version='0.17.7',
     zip_safe=False,
 )

torchkge/__init__.py

@@ -4,7 +4,7 @@
 
 __author__ = """Armand Boschin"""
 __email__ = '[email protected]'
-__version__ = '0.17.6'
+__version__ = '0.17.7'
 
 from torchkge.exceptions import NotYetEvaluatedError
 from torchkge.utils import MarginLoss, LogisticLoss
@@ -13,5 +13,5 @@
 from .evaluation import LinkPredictionEvaluator
 from .evaluation import TripletClassificationEvaluator
 from .models import ConvKBModel
-from .models import RESCALModel, DistMultModel
-from .models import TransEModel, TransHModel, TransRModel, TransDModel
+from .models import RESCALModel, DistMultModel, HolEModel, ComplExModel, AnalogyModel
+from .models import TransEModel, TransHModel, TransRModel, TransDModel, TorusEModel

torchkge/utils/__init__.py

@@ -14,4 +14,5 @@
 from .losses import MarginLoss, LogisticLoss, BinaryCrossEntropyLoss
 from .modeling import init_embedding, get_true_targets, load_embeddings, filter_scores
 from .operations import get_rank, get_mask, get_bernoulli_probs
+from .pretrained_models import load_pretrained_transe, load_pretrained_rescal, load_pretrained_complex
 from .training import Trainer, TrainDataLoader

torchkge/utils/pretrained_models.py

@@ -5,17 +5,17 @@
 """
 
 from ..exceptions import NoPreTrainedVersionError
-from ..models import TransEModel, ComplExModel
+from ..models import TransEModel, ComplExModel, RESCALModel
 from ..utils import load_embeddings
 
 
-def load_pretrained_transe(dataset, emb_dim, data_home=None):
+def load_pretrained_transe(dataset, emb_dim=None, data_home=None):
     """Load a pretrained version of TransE model.
 
     Parameters
     ----------
     dataset: str
-    emb_dim: int
+    emb_dim: int (opt, default None)
         Embedding dimension
     data_home: str (opt, default None)
         Path to the `torchkge_data` directory (containing data folders). Useful
@@ -26,16 +26,18 @@ def load_pretrained_transe(dataset, emb_dim, data_home=None):
     model: `TorchKGE.model.translation.TransEModel`
         Pretrained version of TransE model.
     """
+    dims = {'fb15k': 100, 'wn18rr': 100, 'fb15k237': 150, 'wdv5': 150, 'yago310': 200}
     try:
-        assert (dataset in {'fb15k', 'wn18rr'} and emb_dim == 100) \
-            or (dataset == 'fb15k237' and emb_dim == 150) \
-            or (dataset == 'wdv5' and emb_dim == 150) \
-            or (dataset == 'yago310' and emb_dim == 200)
-
-    except AssertionError:
-        raise NoPreTrainedVersionError('No pre-trained version of TransE for '
-                                       '{} in dimension {}'.format(dataset,
-                                                                   emb_dim))
+        if emb_dim is None:
+            emb_dim = dims[dataset]
+        else:
+            try:
+                assert dims[dataset] == emb_dim
+            except AssertionError:
+                raise NoPreTrainedVersionError('No pre-trained version of TransE for '
+                                               '{} in dimension {}'.format(dataset, emb_dim))
+    except KeyError:
+        raise NoPreTrainedVersionError('No pre-trained version of TransE for {}'.format(dataset))
 
     state_dict = load_embeddings('transe', emb_dim, dataset, data_home)
     model = TransEModel(emb_dim,
@@ -47,13 +49,13 @@ def load_pretrained_transe(dataset, emb_dim, data_home=None):
     return model
 
 
-def load_pretrained_complex(dataset, emb_dim, data_home=None):
+def load_pretrained_complex(dataset, emb_dim=None, data_home=None):
     """Load a pretrained version of ComplEx model.
 
     Parameters
     ----------
     dataset: str
-    emb_dim: int
+    emb_dim: int (opt, default None)
         Embedding dimension
     data_home: str (opt, default None)
         Path to the `torchkge_data` directory (containing data folders). Useful
@@ -64,15 +66,18 @@ def load_pretrained_complex(dataset, emb_dim, data_home=None):
     model: `TorchKGE.model.translation.ComplExModel`
         Pretrained version of ComplEx model.
     """
+    dims = {'wn18rr': 200, 'fb15k237': 200, 'wdv5': 200, 'yago310': 200}
     try:
-        assert (dataset == 'wn18rr' and emb_dim == 200) \
-            or (dataset == 'fb15k237' and emb_dim == 200) \
-            or (dataset == 'wdv5' and emb_dim == 200)
-
-    except AssertionError:
-        raise NoPreTrainedVersionError('No pre-trained version of ComplEx for '
-                                       '{} in dimension {}'.format(dataset,
-                                                                   emb_dim))
+        if emb_dim is None:
+            emb_dim = dims[dataset]
+        else:
+            try:
+                assert dims[dataset] == emb_dim
+            except AssertionError:
+                raise NoPreTrainedVersionError('No pre-trained version of ComplEx for '
+                                               '{} in dimension {}'.format(dataset, emb_dim))
+    except KeyError:
+        raise NoPreTrainedVersionError('No pre-trained version of ComplEx for {}'.format(dataset))
 
     state_dict = load_embeddings('complex', emb_dim, dataset, data_home)
     model = ComplExModel(emb_dim,
@@ -81,3 +86,42 @@ def load_pretrained_complex(dataset, emb_dim, data_home=None):
     model.load_state_dict(state_dict)
 
     return model
+
+
+def load_pretrained_rescal(dataset, emb_dim=None, data_home=None):
+    """Load a pretrained version of RESCAL model.
+
+    Parameters
+    ----------
+    dataset: str
+    emb_dim: int (opt, default None)
+        Embedding dimension
+    data_home: str (opt, default None)
+        Path to the `torchkge_data` directory (containing data folders). Useful
+        for pre-trained model loading.
+
+    Returns
+    -------
+    model: `TorchKGE.model.translation.RESCALModel`
+        Pretrained version of RESCAL model.
+    """
+    dims = {'wn18rr': 200, 'fb15k237': 200, 'yago310': 200}
+    try:
+        if emb_dim is None:
+            emb_dim = dims[dataset]
+        else:
+            try:
+                assert dims[dataset] == emb_dim
+            except AssertionError:
+                raise NoPreTrainedVersionError('No pre-trained version of RESCAL for '
+                                               '{} in dimension {}'.format(dataset, emb_dim))
+    except KeyError:
+        raise NoPreTrainedVersionError('No pre-trained version of RESCAL for {}'.format(dataset))
+
+    state_dict = load_embeddings('rescal', emb_dim, dataset, data_home)
+    model = RESCALModel(emb_dim,
+                         n_entities=state_dict['ent_emb.weight'].shape[0],
+                         n_relations=state_dict['rel_mat.weight'].shape[0])
+    model.load_state_dict(state_dict)
+
+    return model