[KGE] Kg loader (#1300)

* Now dataset accepts user define datasets

* UPdate README

* Fix eval

* Fix

* Fix Freebase

* Fix

* Fix

* upd

* upd

* Update README

* Update some docstrings.

* upd
Co-authored-by: Ubuntu <ubuntu@ip-172-31-8-26.us-east-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-19-78.us-east-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-60-78.ec2.internal>

apps/kg/README.md

@@ -42,9 +42,9 @@ We will support multi-GPU training and distributed training in a near future.
 The package can run with both Pytorch and MXNet. For Pytorch, it works with Pytorch v1.2 or newer.
 For MXNet, it works with MXNet 1.5 or newer.
 
-## Datasets
+## Built-in Datasets
 
-DGL-KE provides five knowledge graphs:
+DGL-KE provides five built-in knowledge graphs:
 
 | Dataset | #nodes | #edges | #relations |
 |---------|--------|--------|------------|
@@ -129,24 +129,20 @@ when given (?, rel, tail).
 
 ### Input formats:
 
-DGL-KE supports two knowledge graph input formats. A knowledge graph is stored
-using five files.
+DGL-KE supports two knowledge graph input formats for user defined dataset
 
-Format 1:
-
-- entities.dict contains pairs of (entity Id, entity name). The number of rows is the number of entities (nodes).
-- relations.dict contains pairs of (relation Id, relation name). The number of rows is the number of relations.
-- train.txt stores edges in the training set. They are stored as triples of (head, rel, tail).
-- valid.txt stores edges in the validation set. They are stored as triples of (head, rel, tail).
-- test.txt stores edges in the test set. They are stored as triples of (head, rel, tail).
+- raw_udd_[h|r|t], raw user defined dataset. In this format, user only need to provide triples and let the dataloader generate and manipulate the id mapping. The dataloader will generate two files: entities.tsv for entity id mapping and relations.tsv for relation id mapping. The order of head, relation and tail entities are described in [h|r|t], for example, raw_udd_trh means the triples are stored in the order of tail, relation and head. It should contains three files:
+  - *train* stores the triples in the training set. In format of a triple, e.g., [src_name, rel_name, dst_name] and should follow the order specified in [h|r|t]
+  - *valid* stores the triples in the validation set. In format of a triple, e.g., [src_name, rel_name, dst_name] and should follow the order specified in [h|r|t]
+  - *test* stores the triples in the test set. In format of a triple, e.g., [src_name, rel_name, dst_name] and should follow the order specified in [h|r|t]
 
 Format 2:
-
-- entity2id.txt contains pairs of (entity name, entity Id). The number of rows is the number of entities (nodes).
-- relation2id.txt contains pairs of (relation name, relation Id). The number of rows is the number of relations.
-- train.txt stores edges in the training set. They are stored as triples of (head, tail, rel).
-- valid.txt stores edges in the validation set. They are stored as a triple of (head, tail, rel).
-- test.txt stores edges in the test set. They are stored as a triple of (head, tail, rel).
+- udd_[h|r|t], user defined dataset. In this format, user should provide the id mapping for entities and relations. The order of head, relation and tail entities are described in [h|r|t], for example, raw_udd_trh means the triples are stored in the order of tail, relation and head. It should contains five files:
+  - *entities* stores the mapping between entity name and entity Id
+  - *relations* stores the mapping between relation name relation Id
+  - *train* stores the triples in the training set. In format of a triple, e.g., [src_id, rel_id, dst_id] and should follow the order specified in [h|r|t]
+  - *valid* stores the triples in the validation set. In format of a triple, e.g., [src_id, rel_id, dst_id] and should follow the order specified in [h|r|t]
+  - *test* stores the triples in the test set. In format of a triple, e.g., [src_id, rel_id, dst_id] and should follow the order specified in [h|r|t]
 
 ### Output formats:
 

apps/kg/dataloader/KGDataset.py

@@ -23,63 +23,86 @@ def _download_and_extract(url, path, filename):
                     writer.write(chunk)
             print('Download finished. Unzipping the file...')
 
-class KGDataset1:
-    '''Load a knowledge graph with format 1
-
-    In this format, the folder with a knowledge graph has five files:
-    * entities.dict stores the mapping between entity Id and entity name.
-    * relations.dict stores the mapping between relation Id and relation name.
-    * train.txt stores the triples in the training set.
-    * valid.txt stores the triples in the validation set.
-    * test.txt stores the triples in the test set.
+def _get_id(dict, key):
+    id = dict.get(key, None)
+    if id is None:
+        id = len(dict)
+        dict[key] = id
+    return id
+
+def _parse_srd_format(format):
+    if format == "hrt":
+        return [0, 1, 2]
+    if format == "htr":
+        return [0, 2, 1]
+    if format == "rht":
+        return [1, 0, 2]
+    if format == "rth":
+        return [2, 0, 1]
+    if format == "thr":
+        return [1, 2, 0]
+    if format == "trh":
+        return [2, 1, 0]
+
+class KGDataset:
+    '''Load a knowledge graph
+
+    The folder with a knowledge graph has five files:
+    * entities stores the mapping between entity Id and entity name.
+    * relations stores the mapping between relation Id and relation name.
+    * train stores the triples in the training set.
+    * valid stores the triples in the validation set.
+    * test stores the triples in the test set.
 
     The mapping between entity (relation) Id and entity (relation) name is stored as 'id\tname'.
 
     The triples are stored as 'head_name\trelation_name\ttail_name'.
     '''
-    def __init__(self, path, name, read_triple=True, only_train=False):
-        url = 'https://data.dgl.ai/dataset/{}.zip'.format(name)
+    def __init__(self, entity_path, relation_path,
+                 train_path, valid_path=None, test_path=None,
+                 format=[0,1,2], read_triple=True, only_train=False,
+                 skip_first_line=False):
+        self.entity2id, self.n_entities = self.read_entity(entity_path)
+        self.relation2id, self.n_relations = self.read_relation(relation_path)
 
-        if not os.path.exists(os.path.join(path, name)):
-            print('File not found. Downloading from', url)
-            _download_and_extract(url, path, name + '.zip')
-        path = os.path.join(path, name)
+        if read_triple == True:
+            self.train = self.read_triple(train_path, "train", skip_first_line, format)
+            if only_train == False:
+                self.valid = self.read_triple(valid_path, "valid", skip_first_line, format)
+                self.test = self.read_triple(test_path, "test", skip_first_line, format)
 
-        with open(os.path.join(path, 'entities.dict')) as f:
+    def read_entity(self, entity_path):
+        with open(entity_path) as f:
             entity2id = {}
             for line in f:
                 eid, entity = line.strip().split('\t')
                 entity2id[entity] = int(eid)
 
-        self.entity2id = entity2id
+        return entity2id, len(entity2id)
 
-        with open(os.path.join(path, 'relations.dict')) as f:
+    def read_relation(self, relation_path):
+        with open(relation_path) as f:
             relation2id = {}
             for line in f:
                 rid, relation = line.strip().split('\t')
                 relation2id[relation] = int(rid)
 
-        self.relation2id = relation2id
+        return relation2id, len(relation2id)
 
-        # TODO: to deal with contries dataset.
-
-        self.n_entities = len(self.entity2id)
-        self.n_relations = len(self.relation2id)
-
-        if read_triple == True:
-            self.train = self.read_triple(path, 'train')
-            if only_train == False:
-                self.valid = self.read_triple(path, 'valid')
-                self.test = self.read_triple(path, 'test')
-
-    def read_triple(self, path, mode):
+    def read_triple(self, path, mode, skip_first_line=False, format=[0,1,2]):
         # mode: train/valid/test
+        if path is None:
+            return None
+
         heads = []
         tails = []
         rels = []
-        with open(os.path.join(path, '{}.txt'.format(mode))) as f:
+        with open(path) as f:
+            if skip_first_line:
+                _ = f.readline()
             for line in f:
-                h, r, t = line.strip().split('\t')
+                triple = line.strip().split('\t')
+                h, r, t = triple[format[0]], triple[format[1]], triple[format[2]]
                 heads.append(self.entity2id[h])
                 rels.append(self.relation2id[r])
                 tails.append(self.entity2id[t])
@@ -89,11 +112,130 @@ class KGDataset1:
 
         return (heads, rels, tails)
 
+class KGDatasetFB15k(KGDataset):
+    '''Load a knowledge graph FB15k
+
+    The FB15k dataset has five files:
+    * entities.dict stores the mapping between entity Id and entity name.
+    * relations.dict stores the mapping between relation Id and relation name.
+    * train.txt stores the triples in the training set.
+    * valid.txt stores the triples in the validation set.
+    * test.txt stores the triples in the test set.
+
+    The mapping between entity (relation) name and entity (relation) Id is stored as 'name\tid'.
+    The triples are stored as 'head_nid\trelation_id\ttail_nid'.
+    '''
+    def __init__(self, path, name='FB15k', read_triple=True, only_train=False):
+        self.name = name
+        url = 'https://data.dgl.ai/dataset/{}.zip'.format(name)
+
+        if not os.path.exists(os.path.join(path, name)):
+            print('File not found. Downloading from', url)
+            _download_and_extract(url, path, name + '.zip')
+        self.path = os.path.join(path, name)
+
+        super(KGDatasetFB15k, self).__init__(os.path.join(self.path, 'entities.dict'),
+                                             os.path.join(self.path, 'relations.dict'),
+                                             os.path.join(self.path, 'train.txt'),
+                                             os.path.join(self.path, 'valid.txt'),
+                                             os.path.join(self.path, 'test.txt'),
+                                             read_triple=read_triple,
+                                             only_train=only_train)
 
-class KGDataset2:
-    '''Load a knowledge graph with format 2
+class KGDatasetFB15k237(KGDataset):
+    '''Load a knowledge graph FB15k-237
+
+    The FB15k-237 dataset has five files:
+    * entities.dict stores the mapping between entity Id and entity name.
+    * relations.dict stores the mapping between relation Id and relation name.
+    * train.txt stores the triples in the training set.
+    * valid.txt stores the triples in the validation set.
+    * test.txt stores the triples in the test set.
 
-    In this format, the folder with a knowledge graph has five files:
+    The mapping between entity (relation) name and entity (relation) Id is stored as 'name\tid'.
+    The triples are stored as 'head_nid\trelation_id\ttail_nid'.
+    '''
+    def __init__(self, path, name='FB15k-237', read_triple=True, only_train=False):
+        self.name = name
+        url = 'https://data.dgl.ai/dataset/{}.zip'.format(name)
+
+        if not os.path.exists(os.path.join(path, name)):
+            print('File not found. Downloading from', url)
+            _download_and_extract(url, path, name + '.zip')
+        self.path = os.path.join(path, name)
+
+        super(KGDatasetFB15k237, self).__init__(os.path.join(self.path, 'entities.dict'),
+                                                os.path.join(self.path, 'relations.dict'),
+                                                os.path.join(self.path, 'train.txt'),
+                                                os.path.join(self.path, 'valid.txt'),
+                                                os.path.join(self.path, 'test.txt'),
+                                                read_triple=read_triple,
+                                                only_train=only_train)
+
+class KGDatasetWN18(KGDataset):
+    '''Load a knowledge graph wn18
+
+    The wn18 dataset has five files:
+    * entities.dict stores the mapping between entity Id and entity name.
+    * relations.dict stores the mapping between relation Id and relation name.
+    * train.txt stores the triples in the training set.
+    * valid.txt stores the triples in the validation set.
+    * test.txt stores the triples in the test set.
+
+    The mapping between entity (relation) name and entity (relation) Id is stored as 'name\tid'.
+    The triples are stored as 'head_nid\trelation_id\ttail_nid'.
+    '''
+    def __init__(self, path, name='wn18', read_triple=True, only_train=False):
+        self.name = name
+        url = 'https://data.dgl.ai/dataset/{}.zip'.format(name)
+
+        if not os.path.exists(os.path.join(path, name)):
+            print('File not found. Downloading from', url)
+            _download_and_extract(url, path, name + '.zip')
+        self.path = os.path.join(path, name)
+
+        super(KGDatasetWN18, self).__init__(os.path.join(self.path, 'entities.dict'),
+                                            os.path.join(self.path, 'relations.dict'),
+                                            os.path.join(self.path, 'train.txt'),
+                                            os.path.join(self.path, 'valid.txt'),
+                                            os.path.join(self.path, 'test.txt'),
+                                            read_triple=read_triple,
+                                            only_train=only_train)
+
+class KGDatasetWN18rr(KGDataset):
+    '''Load a knowledge graph wn18rr
+
+    The wn18rr dataset has five files:
+    * entities.dict stores the mapping between entity Id and entity name.
+    * relations.dict stores the mapping between relation Id and relation name.
+    * train.txt stores the triples in the training set.
+    * valid.txt stores the triples in the validation set.
+    * test.txt stores the triples in the test set.
+
+    The mapping between entity (relation) name and entity (relation) Id is stored as 'name\tid'.
+    The triples are stored as 'head_nid\trelation_id\ttail_nid'.
+    '''
+    def __init__(self, path, name='wn18rr', read_triple=True, only_train=False):
+        self.name = name
+        url = 'https://data.dgl.ai/dataset/{}.zip'.format(name)
+
+        if not os.path.exists(os.path.join(path, name)):
+            print('File not found. Downloading from', url)
+            _download_and_extract(url, path, name + '.zip')
+        self.path = os.path.join(path, name)
+
+        super(KGDatasetWN18rr, self).__init__(os.path.join(self.path, 'entities.dict'),
+                                              os.path.join(self.path, 'relations.dict'),
+                                              os.path.join(self.path, 'train.txt'),
+                                              os.path.join(self.path, 'valid.txt'),
+                                              os.path.join(self.path, 'test.txt'),
+                                              read_triple=read_triple,
+                                              only_train=only_train)
+
+class KGDatasetFreebase(KGDataset):
+    '''Load a knowledge graph Full Freebase
+
+    The Freebase dataset has five files:
     * entity2id.txt stores the mapping between entity name and entity Id.
     * relation2id.txt stores the mapping between relation name relation Id.
     * train.txt stores the triples in the training set.
@@ -101,10 +243,10 @@ class KGDataset2:
     * test.txt stores the triples in the test set.
 
     The mapping between entity (relation) name and entity (relation) Id is stored as 'name\tid'.
-
     The triples are stored as 'head_nid\trelation_id\ttail_nid'.
     '''
-    def __init__(self, path, name, read_triple=True, only_train=False):
+    def __init__(self, path, name='Freebase', read_triple=True, only_train=False):
+        self.name = name
         url = 'https://data.dgl.ai/dataset/{}.zip'.format(name)
 
         if not os.path.exists(os.path.join(path, name)):
@@ -112,31 +254,30 @@ class KGDataset2:
             _download_and_extract(url, path, '{}.zip'.format(name))
         self.path = os.path.join(path, name)
 
-        f_rel2id = os.path.join(self.path, 'relation2id.txt')
-        with open(f_rel2id) as f_rel:
-            self.n_relations = int(f_rel.readline()[:-1])
+        super(KGDatasetFreebase, self).__init__(os.path.join(self.path, 'entity2id.txt'),
+                                                os.path.join(self.path, 'relation2id.txt'),
+                                                os.path.join(self.path, 'train.txt'),
+                                                os.path.join(self.path, 'valid.txt'),
+                                                os.path.join(self.path, 'test.txt'),
+                                                read_triple=read_triple,
+                                                only_train=only_train)
 
-        if only_train == True:
-            f_ent2id = os.path.join(self.path, 'local_to_global.txt')
-            with open(f_ent2id) as f_ent:
-                self.n_entities = len(f_ent.readlines())
-        else:
-            f_ent2id = os.path.join(self.path, 'entity2id.txt')
-            with open(f_ent2id) as f_ent:
-                self.n_entities = int(f_ent.readline()[:-1])
+    def read_entity(self, entity_path):
+        with open(entity_path) as f_ent:
+            n_entities = int(f_ent.readline()[:-1])
+        return None, n_entities
 
-        if read_triple == True:
-            self.train = self.read_triple(self.path, 'train')
-            if only_train == False:
-                self.valid = self.read_triple(self.path, 'valid')
-                self.test = self.read_triple(self.path, 'test')
+    def read_relation(self, relation_path):
+        with open(relation_path) as f_rel:
+            n_relations = int(f_rel.readline()[:-1])
+        return None, n_relations
 
     def read_triple(self, path, mode, skip_first_line=False):
         heads = []
         tails = []
         rels = []
         print('Reading {} triples....'.format(mode))
-        with open(os.path.join(path, '{}.txt'.format(mode))) as f:
+        with open(path) as f:
             if skip_first_line:
                 _ = f.readline()
             for line in f:
@@ -150,27 +291,203 @@ class KGDataset2:
         print('Finished. Read {} {} triples.'.format(len(heads), mode))
         return (heads, rels, tails)
 
+class KGDatasetUDDRaw(KGDataset):
+    '''Load a knowledge graph user defined dataset
+
+    The user defined dataset has five files:
+    * entities stores the mapping between entity name and entity Id.
+    * relations stores the mapping between relation name relation Id.
+    * train stores the triples in the training set. In format [src_name, rel_name, dst_name]
+    * valid stores the triples in the validation set. In format [src_name, rel_name, dst_name]
+    * test stores the triples in the test set. In format [src_name, rel_name, dst_name]
+
+    The mapping between entity (relation) name and entity (relation) Id is stored as 'name\tid'.
+    The triples are stored as 'head_nid\trelation_id\ttail_nid'.
+    '''
+    def __init__(self, path, name, files, format):
+        self.name = name
+        for f in files:
+            assert os.path.exists(os.path.join(path, f)), \
+                'File {} now exist in {}'.format(f, path)
+
+        assert len(format) == 3
+        format = _parse_srd_format(format)
+        self.load_entity_relation(path, files, format)
+
+        # Only train set is provided
+        if len(files) == 1:
+            super(KGDatasetUDDRaw, self).__init__("entities.tsv",
+                                                  "relation.tsv",
+                                                  os.path.join(path, files[0]),
+                                                  format=format,
+                                                  read_triple=True,
+                                                  only_train=True)
+        # Train, validation and test set are provided
+        if len(files) == 3:
+            super(KGDatasetUDDRaw, self).__init__("entities.tsv",
+                                                  "relation.tsv",
+                                                  os.path.join(path, files[0]),
+                                                  os.path.join(path, files[1]),
+                                                  os.path.join(path, files[2]),
+                                                  format=format,
+                                                  read_triple=True,
+                                                  only_train=False)
+
+    def load_entity_relation(self, path, files, format):
+        entity_map = {}
+        rel_map = {}
+        for fi in files:
+            with open(os.path.join(path, fi)) as f:
+                for line in f:
+                    triple = line.strip().split('\t')
+                    src, rel, dst = triple[format[0]], triple[format[1]], triple[format[2]]
+                    src_id = _get_id(entity_map, src)
+                    dst_id = _get_id(entity_map, dst)
+                    rel_id = _get_id(rel_map, rel)
+
+        entities = ["{}\t{}\n".format(key, val) for key, val in entity_map.items()]
+        with open(os.path.join(path, "entities.tsv"), "w+") as f:
+            f.writelines(entities)
+        self.entity2id = entity_map
+        self.n_entities = len(entities)
+
+        relations = ["{}\t{}\n".format(key, val) for key, val in rel_map.items()]
+        with open(os.path.join(path, "relations.tsv"), "w+") as f:
+            f.writelines(relations)
+        self.relation2id = rel_map
+        self.n_relations = len(relations)
+
+    def read_entity(self, entity_path):
+        return self.entity2id, self.n_entities
+    
+    def read_relation(self, relation_path):
+        return self.relation2id, self.n_relations
+
+class KGDatasetUDD(KGDataset):
+    '''Load a knowledge graph user defined dataset
+
+    The user defined dataset has five files:
+    * entities stores the mapping between entity name and entity Id.
+    * relations stores the mapping between relation name relation Id.
+    * train stores the triples in the training set. In format [src_id, rel_id, dst_id]
+    * valid stores the triples in the validation set. In format [src_id, rel_id, dst_id]
+    * test stores the triples in the test set. In format [src_id, rel_id, dst_id]
+
+    The mapping between entity (relation) name and entity (relation) Id is stored as 'name\tid'.
+    The triples are stored as 'head_nid\trelation_id\ttail_nid'.
+    '''
+    def __init__(self, path, name, files, format, read_triple=True, only_train=False):
+        self.name = name
+        for f in files:
+            assert os.path.exists(os.path.join(path, f)), \
+                'File {} now exist in {}'.format(f, path)
+
+        format = _parse_srd_format(format)
+        if len(files) == 3:
+            super(KGDatasetUDD, self).__init__(os.path.join(path, files[0]),
+                                               os.path.join(path, files[1]),
+                                               os.path.join(path, files[2]),
+                                               os.path.join(path, None),
+                                               os.path.join(path, None),
+                                               format=format,
+                                               read_triple=read_triple,
+                                               only_train=only_train)
+        if len(files) == 5:
+            super(KGDatasetUDD, self).__init__(os.path.join(path, files[0]),
+                                               os.path.join(path, files[1]),
+                                               os.path.join(path, files[2]),
+                                               os.path.join(path, files[3]),
+                                               os.path.join(path, files[4]),
+                                               format=format,
+                                               read_triple=read_triple,
+                                               only_train=only_train)
+
+    def read_entity(self, entity_path):
+        n_entities = 0
+        with open(entity_path) as f_ent:
+            for line in f_ent:
+                n_entities += 1
+        return None, n_entities
+
+    def read_relation(self, relation_path):
+        n_relations = 0
+        with open(relation_path) as f_rel:
+            for line in f_rel:
+                n_relations += 1
+        return None, n_relations
+
+    def read_triple(self, path, mode, skip_first_line=False, format=[0,1,2]):
+        heads = []
+        tails = []
+        rels = []
+        print('Reading {} triples....'.format(mode))
+        with open(path) as f:
+            if skip_first_line:
+                _ = f.readline()
+            for line in f:
+                triple = line.strip().split('\t')
+                h, r, t = triple[format[0]], triple[format[1]], triple[format[2]]
+                heads.append(int(h))
+                tails.append(int(t))
+                rels.append(int(r))
+        heads = np.array(heads, dtype=np.int64)
+        tails = np.array(tails, dtype=np.int64)
+        rels = np.array(rels, dtype=np.int64)
+        print('Finished. Read {} {} triples.'.format(len(heads), mode))
+        return (heads, rels, tails)
 
-def get_dataset(data_path, data_name, format_str):
-    if data_name == 'Freebase':
-        dataset = KGDataset2(data_path, data_name)
-    elif format_str == '1':
-        dataset = KGDataset1(data_path, data_name)
+def get_dataset(data_path, data_name, format_str, files=None):
+    if format_str == 'built_in':
+        if data_name == 'Freebase':
+            dataset = KGDatasetFreebase(data_path)
+        elif data_name == 'FB15k':
+            dataset = KGDatasetFB15k(data_path)
+        elif data_name == 'FB15k-237':
+            dataset = KGDatasetFB15k237(data_path)
+        elif data_name == 'wn18':
+            dataset = KGDatasetWN18(data_path)
+        elif data_name == 'wn18rr':
+            dataset = KGDatasetWN18rr(data_path)
+        else: 
+            assert False, "Unknown dataset {}".format(data_name)
+    elif format_str.startswith('raw_udd'):
+        # user defined dataset
+        format = format_str[8:]
+        dataset = KGDatasetUDDRaw(data_path, data_name, files, format)
+    elif format_str.startswith('udd'):
+        # user defined dataset
+        format = format_str[4:]
+        dataset = KGDatasetUDD(data_path, data_name, files, format)
     else:
-        dataset = KGDataset2(data_path, data_name)
+        assert False, "Unknown format {}".format(format_str)
 
     return dataset
 
 
 def get_partition_dataset(data_path, data_name, format_str, part_id):
     part_name = os.path.join(data_name, 'part_'+str(part_id))
-
-    if data_name == 'Freebase':
-        dataset = KGDataset2(data_path, part_name, read_triple=True, only_train=True)
-    elif format_str == '1':
-        dataset = KGDataset1(data_path, part_name, read_triple=True, only_train=True)
+    if format_str == 'built_in':
+        if data_name == 'Freebase':
+            dataset = KGDatasetFreebase(data_path, part_name, read_triple=True, only_train=True)
+        elif data_name == 'FB15k':
+            dataset = KGDatasetFB15k(data_path, part_name, read_triple=True, only_train=True)
+        elif data_name == 'FB15k-237':
+            dataset = KGDatasetFB15k237(data_path, part_name, read_triple=True, only_train=True)
+        elif data_name == 'wn18':
+            dataset = KGDatasetWN18(data_path, part_name, read_triple=True, only_train=True)
+        elif data_name == 'wn18rr':
+            dataset = KGDatasetWN18rr(data_path, part_name, read_triple=True, only_train=True)
+        else:
+            assert False, "Unknown dataset {}".format(data_name)
+    elif format_str == 'raw_udd':
+        # user defined dataset
+        assert False, "When using partitioned dataset, we assume dataset will not be raw"
+    elif format_str == 'udd':
+        # user defined dataset
+        format = format_str[4:]
+        dataset = KGDatasetUDD(data_path, data_name, files, format, read_triple=True, only_train=True)
     else:
-        dataset = KGDataset2(data_path, part_name, read_triple=True, only_train=True)
+        assert False, "Unknown format {}".format(format_str)
 
     path = os.path.join(data_path, part_name)
 
@@ -186,16 +503,31 @@ def get_partition_dataset(data_path, data_name, format_str, part_id):
 
     return dataset, partition_book, local_to_global
 
-
 def get_server_partition_dataset(data_path, data_name, format_str, part_id):
     part_name = os.path.join(data_name, 'part_'+str(part_id))
 
-    if data_name == 'Freebase':
-        dataset = KGDataset2(data_path, part_name, read_triple=False, only_train=True)
-    elif format_str == '1':
-        dataset = KGDataset1(data_path, part_name, read_triple=False, only_train=True)
+    if format_str == 'built_in':
+        if data_name == 'Freebase':
+            dataset = KGDatasetFreebase(data_path, part_name, read_triple=False, only_train=True)
+        elif data_name == 'FB15k':
+            dataset = KGDatasetFB15k(data_path, part_name, read_triple=False, only_train=True)
+        elif data_name == 'FB15k-237':
+            dataset = KGDatasetFB15k237(data_path, part_name, read_triple=False, only_train=True)
+        elif data_name == 'wn18':
+            dataset = KGDatasetWN18(data_path, part_name, read_triple=False, only_train=True)
+        elif data_name == 'wn18rr':
+            dataset = KGDatasetWN18rr(data_path, part_name, read_triple=False, only_train=True)
+        else:
+            assert False, "Unknown dataset {}".format(data_name)
+    elif format_str == 'raw_udd':
+        # user defined dataset
+        assert False, "When using partitioned dataset, we assume dataset will not be raw"
+    elif format_str == 'udd':
+        # user defined dataset
+        format = format_str[4:]
+        dataset = KGDatasetUDD(data_path, data_name, files, format, read_triple=False, only_train=True)
     else:
-        dataset = KGDataset2(data_path, part_name, read_triple=False, only_train=True)
+        assert False, "Unknown format {}".format(format_str)
 
     path = os.path.join(data_path, part_name)
 

apps/kg/eval.py

@@ -29,8 +29,11 @@ class ArgParser(argparse.ArgumentParser):
                           help='root path of all dataset')
         self.add_argument('--dataset', type=str, default='FB15k',
                           help='dataset name, under data_path')
-        self.add_argument('--format', type=str, default='1',
-                          help='the format of the dataset.')
+        self.add_argument('--format', type=str, default='built_in',
+                          help='the format of the dataset, it can be built_in,'\
+                                'raw_udd_{htr} and udd_{htr}')
+        self.add_argument('--data_files', type=str, default=None, nargs='+',
+                          help='a list of data files, e.g. entity relation train valid test')
         self.add_argument('--model_path', type=str, default='ckpts',
                           help='the place where models are saved')
 
@@ -97,7 +100,7 @@ def main(args):
         assert not args.eval_filter, "if negative sampling based on degree, we can't filter positive edges."
 
     # load dataset and samplers
-    dataset = get_dataset(args.data_path, args.dataset, args.format)
+    dataset = get_dataset(args.data_path, args.dataset, args.format, args.data_files)
     args.pickle_graph = False
     args.train = False
     args.valid = False

apps/kg/train.py

@@ -31,8 +31,11 @@ class ArgParser(argparse.ArgumentParser):
                           help='root path of all dataset')
         self.add_argument('--dataset', type=str, default='FB15k',
                           help='dataset name, under data_path')
-        self.add_argument('--format', type=str, default='1',
-                          help='the format of the dataset.')
+        self.add_argument('--format', type=str, default='built_in',
+                          help='the format of the dataset, it can be built_in,'\
+                                'raw_udd_{htr} and udd_{htr}')
+        self.add_argument('--data_files', type=str, default=None, nargs='+',
+                          help='a list of data files, e.g. entity relation train valid test')
         self.add_argument('--save_path', type=str, default='ckpts',
                           help='place to save models and logs')
         self.add_argument('--save_emb', type=str, default=None,
@@ -156,7 +159,7 @@ def get_logger(args):
 def run(args, logger):
     train_time_start = time.time()
     # load dataset and samplers
-    dataset = get_dataset(args.data_path, args.dataset, args.format)
+    dataset = get_dataset(args.data_path, args.dataset, args.format, args.data_files)
     n_entities = dataset.n_entities
     n_relations = dataset.n_relations
     if args.neg_sample_size_test < 0:

apps/kg/train_pytorch.py

@@ -189,7 +189,6 @@ def test(args, model, test_samplers, rank=0, mode='Test', queue=None):
     with th.no_grad():
         logs = []
         for sampler in test_samplers:
-            count = 0
             for pos_g, neg_g in sampler:
                 model.forward_test(pos_g, neg_g, logs, gpu_id)