def load_custom(data_path=None, split_size=[0.8, 0.1, 0.1]):
"""Load FB15k dataset. See `here
<https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data>`__
for paper by Bordes et al. originally presenting the dataset.
Parameters
----------
data_home: str, optional
Path to the `torchkge_data` directory (containing data folders). If
files are not present on disk in this directory, they are downloaded
and then placed in the right place.
Returns
-------
kg_train: torchkge.data_structures.KnowledgeGraph
kg_val: torchkge.data_structures.KnowledgeGraph
kg_test: torchkge.data_structures.KnowledgeGraph
"""
df = pd.read_csv(data_path,
sep=",",
header=None,
names=["from", "rel", "to"])
df = shuffle(df)
kg = KnowledgeGraph(df)
train_size = int(len(df) * split_size[0])
test_size = int(len(df) * split_size[1])
valid_size = len(df) - (train_size + test_size)
return kg.split_kg(sizes=(train_size, test_size, valid_size))
def load_fb15k237(data_home=None):
"""Load fb15k237 dataset. See `here
<https://www.aclweb.org/anthology/D15-1174/>`__ for paper by Toutanova et
al. originally presenting the dataset.
Parameters
----------
data_home: str, optional
Path to the `torchkge_data` directory (containing data folders). If
files are not present on disk in this directory, they are downloaded
and then placed in the right place.
Returns
-------
kg_train: torchkge.data_structures.KnowledgeGraph
kg_val: torchkge.data_structures.KnowledgeGraph
kg_test: torchkge.data_structures.KnowledgeGraph
"""
if data_home is None:
data_home = get_data_home()
data_path = data_home + '/FB15k237'
if not exists(data_path):
makedirs(data_path, exist_ok=True)
urlretrieve(
"https://graphs.telecom-paristech.fr/datasets/FB15k237.zip",
data_home + '/FB15k237.zip')
with zipfile.ZipFile(data_home + '/FB15k237.zip', 'r') as zip_ref:
zip_ref.extractall(data_home)
remove(data_home + '/FB15k237.zip')
shutil.rmtree(data_home + '/__MACOSX')
df1 = read_csv(data_path + '/train.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df2 = read_csv(data_path + '/valid.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df3 = read_csv(data_path + '/test.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df = concat([df1, df2, df3])
kg = KnowledgeGraph(df)
return kg.split_kg(sizes=(len(df1), len(df2), len(df3)))
def load_fb15k(data_home=None):
"""Load FB15k dataset. See `here
<https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data>`__
for paper by Bordes et al. originally presenting the dataset.
Parameters
----------
data_home: str, optional
Path to the `torchkge_data` directory (containing data folders). If
files are not present on disk in this directory, they are downloaded
and then placed in the right place.
Returns
-------
kg_train: torchkge.data_structures.KnowledgeGraph
kg_val: torchkge.data_structures.KnowledgeGraph
kg_test: torchkge.data_structures.KnowledgeGraph
"""
if data_home is None:
data_home = get_data_home()
data_path = data_home + '/FB15k'
if not exists(data_path):
makedirs(data_path, exist_ok=True)
urlretrieve(
"https://graphs.telecom-paristech.fr/data/torchkge/kgs/FB15k.zip",
data_home + '/FB15k.zip')
with zipfile.ZipFile(data_home + '/FB15k.zip', 'r') as zip_ref:
zip_ref.extractall(data_home)
remove(data_home + '/FB15k.zip')
shutil.rmtree(data_home + '/__MACOSX')
df1 = read_csv(data_path + '/freebase_mtr100_mte100-train.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df2 = read_csv(data_path + '/freebase_mtr100_mte100-valid.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df3 = read_csv(data_path + '/freebase_mtr100_mte100-test.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df = concat([df1, df2, df3])
kg = KnowledgeGraph(df)
return kg.split_kg(sizes=(len(df1), len(df2), len(df3)))
def test_get_bernoulli_probs(self):
kg = KnowledgeGraph(df=self.df)
probs = get_bernoulli_probs(kg)
res = {0: 0.5714, 1: 0.5, 2: 0.5, 3: 0.5}
for k in probs.keys():
assert (res[k] - probs[k]) < 1e-03
def load_wn18rr(data_home=None):
"""Load WN18RR dataset. See `here
<https://arxiv.org/abs/1707.01476>`__ for paper by Dettmers et
al. originally presenting the dataset.
Parameters
----------
data_home: str, optional
Path to the `torchkge_data` directory (containing data folders). If
files are not present on disk in this directory, they are downloaded
and then placed in the right place.
Returns
-------
kg_train: torchkge.data_structures.KnowledgeGraph
kg_val: torchkge.data_structures.KnowledgeGraph
kg_test: torchkge.data_structures.KnowledgeGraph
"""
if data_home is None:
data_home = get_data_home()
data_path = data_home + '/WN18RR'
if not exists(data_path):
makedirs(data_path, exist_ok=True)
urlretrieve(
"https://graphs.telecom-paristech.fr/data/torchkge/kgs/WN18RR.zip",
data_home + '/WN18RR.zip')
with zipfile.ZipFile(data_home + '/WN18RR.zip', 'r') as zip_ref:
zip_ref.extractall(data_home)
remove(data_home + '/WN18RR.zip')
df1 = read_csv(data_path + '/train.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df2 = read_csv(data_path + '/valid.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df3 = read_csv(data_path + '/test.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df = concat([df1, df2, df3])
kg = KnowledgeGraph(df)
return kg.split_kg(sizes=(len(df1), len(df2), len(df3)))
def load_wn18(data_home=None):
"""Load WN18 dataset.
Parameters
----------
data_home: str, optional
Path to the `torchkge_data` directory (containing data folders). If
files are not present on disk in this directory, they are downloaded
and then placed in the right place.
Returns
-------
kg_train: torchkge.data_structures.KnowledgeGraph
kg_val: torchkge.data_structures.KnowledgeGraph
kg_test: torchkge.data_structures.KnowledgeGraph
"""
if data_home is None:
data_home = get_data_home()
data_path = data_home + '/WN18'
if not exists(data_path):
makedirs(data_path, exist_ok=True)
urlretrieve(
"https://graphs.telecom-paristech.fr/data/torchkge/kgs/WN18.zip",
data_home + '/WN18.zip')
with zipfile.ZipFile(data_home + '/WN18.zip', 'r') as zip_ref:
zip_ref.extractall(data_home)
remove(data_home + '/WN18.zip')
shutil.rmtree(data_home + '/__MACOSX')
df1 = read_csv(data_path + '/wordnet-mlj12-train.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df2 = read_csv(data_path + '/wordnet-mlj12-valid.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df3 = read_csv(data_path + '/wordnet-mlj12-test.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'])
df = concat([df1, df2, df3])
kg = KnowledgeGraph(df)
return kg.split_kg(sizes=(len(df1), len(df2), len(df3)))
def test_KnowledgeGraph_Builder(self):
assert len(self.kg) == 9
assert self.kg.n_ent == 6
assert self.kg.n_rel == 4
assert (type(self.kg.rel2ix) == dict) & (type(self.kg.rel2ix) == dict)
assert (type(self.kg.head_idx) == Tensor) & (type(self.kg.tail_idx) == Tensor) & \
(type(self.kg.relations) == Tensor)
assert (self.kg.head_idx.dtype == int64) & (self.kg.tail_idx.dtype == int64) & \
(self.kg.relations.dtype == int64)
assert (len(self.kg.head_idx) == len(self.kg.tail_idx) == len(self.kg.relations))
kg_dict = {'heads': self.kg.head_idx, 'tails': self.kg.tail_idx, 'relations': self.kg.relations}
with self.assertRaises(WrongArgumentsError):
KnowledgeGraph()
with self.assertRaises(WrongArgumentsError):
KnowledgeGraph(kg=kg_dict, df=self.df)
with self.assertRaises(WrongArgumentsError):
KnowledgeGraph(kg=kg_dict)
with self.assertRaises(WrongArgumentsError):
KnowledgeGraph(kg={'heads': self.kg.head_idx, 'tails': self.kg.tail_idx},
ent2ix=self.kg.ent2ix, rel2ix=self.kg.rel2ix)
with self.assertRaises(SanityError):
KnowledgeGraph(kg={'heads': self.kg.head_idx[:-1],
'tails': self.kg.tail_idx,
'relations': self.kg.relations},
ent2ix=self.kg.ent2ix, rel2ix=self.kg.rel2ix)
with self.assertRaises(SanityError):
KnowledgeGraph(kg={'heads': self.kg.head_idx.int(),
'tails': self.kg.tail_idx,
'relations': self.kg.relations},
ent2ix=self.kg.ent2ix, rel2ix=self.kg.rel2ix)
class TestUtils(unittest.TestCase):
"""Tests for `torchkge.utils`."""
def setUp(self):
self.df = pd.DataFrame([[0, 1, 0], [0, 2, 0], [0, 3, 0], [0, 4, 0], [1, 2, 1], [1, 3, 2], [2, 4, 0], [3, 4, 4],
[5, 4, 0]], columns=['from', 'to', 'rel'])
self.kg = KnowledgeGraph(self.df)
def test_KnowledgeGraph_Builder(self):
assert len(self.kg) == 9
assert self.kg.n_ent == 6
assert self.kg.n_rel == 4
assert (type(self.kg.rel2ix) == dict) & (type(self.kg.rel2ix) == dict)
assert (type(self.kg.head_idx) == Tensor) & (type(self.kg.tail_idx) == Tensor) & \
(type(self.kg.relations) == Tensor)
assert (self.kg.head_idx.dtype == int64) & (self.kg.tail_idx.dtype == int64) & \
(self.kg.relations.dtype == int64)
assert (len(self.kg.head_idx) == len(self.kg.tail_idx) == len(self.kg.relations))
kg_dict = {'heads': self.kg.head_idx, 'tails': self.kg.tail_idx, 'relations': self.kg.relations}
with self.assertRaises(WrongArgumentsError):
KnowledgeGraph()
with self.assertRaises(WrongArgumentsError):
KnowledgeGraph(kg=kg_dict, df=self.df)
with self.assertRaises(WrongArgumentsError):
KnowledgeGraph(kg=kg_dict)
with self.assertRaises(WrongArgumentsError):
KnowledgeGraph(kg={'heads': self.kg.head_idx, 'tails': self.kg.tail_idx},
ent2ix=self.kg.ent2ix, rel2ix=self.kg.rel2ix)
with self.assertRaises(SanityError):
KnowledgeGraph(kg={'heads': self.kg.head_idx[:-1],
'tails': self.kg.tail_idx,
'relations': self.kg.relations},
ent2ix=self.kg.ent2ix, rel2ix=self.kg.rel2ix)
with self.assertRaises(SanityError):
KnowledgeGraph(kg={'heads': self.kg.head_idx.int(),
'tails': self.kg.tail_idx,
'relations': self.kg.relations},
ent2ix=self.kg.ent2ix, rel2ix=self.kg.rel2ix)
def test_split_kg(self):
assert (len(self.kg.split_kg()) == 2) & (len(self.kg.split_kg(validation=True)) == 3)
with self.assertRaises(SizeMismatchError):
self.kg.split_kg(sizes=(1, 2, 3, 4))
with self.assertRaises(WrongArgumentsError):
self.kg.split_kg(sizes=(9, 9, 9))
with self.assertRaises(WrongArgumentsError):
self.kg.split_kg(sizes=(9, 9))
class TestUtils(unittest.TestCase):
def setUp(self):
df = pd.DataFrame(
[[0, 1, 0], [0, 2, 0], [0, 3, 0], [0, 4, 0], [1, 2, 1], [1, 3, 2],
[2, 4, 0], [3, 4, 4], [5, 4, 0]],
columns=['from', 'to', 'rel'])
self.kg = KnowledgeGraph(df)
def checkSanityLinkPrediction(self, evaluator):
assert evaluator.rank_true_heads.dtype == long
assert evaluator.rank_true_tails.dtype == long
assert evaluator.filt_rank_true_heads.dtype == long
assert evaluator.filt_rank_true_tails.dtype == long
assert evaluator.rank_true_heads.shape[0] == len(self.kg)
assert evaluator.rank_true_tails.shape[0] == len(self.kg)
assert evaluator.filt_rank_true_heads.shape[0] == len(self.kg)
assert evaluator.filt_rank_true_tails.shape[0] == len(self.kg)
def test_LinkPredictionEvaluator(self):
model = TransEModel(100, self.kg.n_ent, self.kg.n_rel, 'L1')
evaluator = LinkPredictionEvaluator(model, self.kg)
self.checkSanityLinkPrediction(evaluator)
evaluator.evaluate(b_size=len(self.kg), k_max=10)
self.checkSanityLinkPrediction(evaluator)
def test_TripletClassificationEvaluator(self):
model = TransEModel(100, self.kg.n_ent, self.kg.n_rel, 'L1')
kg1, kg2 = self.kg.split_kg(sizes=(4, 5))
# kg2 contains all relations so it will be used as validation
evaluator = TripletClassificationEvaluator(model, kg2, kg1)
assert evaluator.thresholds is None
assert not evaluator.evaluated
evaluator.evaluate(b_size=len(self.kg))
assert evaluator.evaluated
assert evaluator.thresholds is not None
assert (len(evaluator.thresholds.shape)
== 1) & (evaluator.thresholds.shape[0] == self.kg.n_rel)
def test_get_possible_heads_tails(self):
kg = KnowledgeGraph(self.df)
h, t = get_possible_heads_tails(kg)
assert (type(h) == dict) & (type(t) == dict)
assert h == {0: {0, 2, 5}, 1: {1}, 2: {1}, 3: {3}}
assert t == {0: {1, 2, 3, 4}, 1: {2}, 2: {3}, 3: {4}}
p_h, p_t = defaultdict(set), defaultdict(set)
p_h[0].add(40)
p_h[10].add(50)
p_t[0].add(41)
p_t[10].add(51)
h, t = get_possible_heads_tails(kg,
possible_heads=dict(p_h),
possible_tails=dict(p_t))
assert h == {0: {0, 2, 5, 40}, 1: {1}, 2: {1}, 3: {3}, 10: {50}}
assert t == {0: {1, 2, 3, 4, 41}, 1: {2}, 2: {3}, 3: {4}, 10: {51}}
def load_Sweden(data_home=None, GDR=False):
"""
Parameters
----------
data_home: str, optional
Path to the `torchkge_data` directory (containing data folders). If
files are not present on disk in this directory, they are downloaded
and then placed in the right place.
Returns
-------
kg_train: torchkge.data_structures.KnowledgeGraph
kg_val: torchkge.data_structures.KnowledgeGraph
kg_test: torchkge.data_structures.KnowledgeGraph
"""
if data_home is None:
data_home = get_data_home()
data_path = data_home + '/Sweden'
if GDR == True:
geo = data_path + '/ent2point.txt'
else:
geo = None
if exists(data_path + '/train.txt') and exists(data_path + '/test.txt') and exists(data_path + '/valid.txt'):
df1 = read_csv(data_path + '/train.txt',
sep='\t', header=None, names=['from', 'rel', 'to'])
df2 = read_csv(data_path + '/valid.txt',
sep='\t', header=None, names=['from', 'rel', 'to'])
df3 = read_csv(data_path + '/test.txt',
sep='\t', header=None, names=['from', 'rel', 'to'])
df = concat([df1, df2, df3])
kg = KnowledgeGraph(df=df,geo=geo)
kg_train, kg_val, kg_test = kg.split_kg(sizes=(len(df1), len(df2), len(df3)),geo=geo)
return kg_train, kg_val, kg_test
else:
df = read_csv(data_path + '/triplets.txt',
sep='\t', header=None, names=['from', 'rel', 'to'],encoding='utf-8')
kg = KnowledgeGraph(df=df,geo=geo)
kg_train, kg_val, kg_test = kg.split_kg(share=0.8, validation=True,geo=geo)
data_save('/Sweden',kg_train, kg_val, kg_test,geo=geo)
return kg_train, kg_val, kg_test
def load_wikidata_vitals(level=5, data_home=None):
"""Load knowledge graph extracted from Wikidata using the entities
corresponding to Wikipedia pages contained in Wikivitals. See `here
<https://netset.telecom-paris.fr/>`__ for details on Wikivitals and
Wikivitals+ datasets.
Parameters
----------
level: int (default=5)
Either 4 or 5.
data_home: str, optional
Path to the `torchkge_data` directory (containing data folders). If
files are not present on disk in this directory, they are downloaded
and then placed in the right place.
Returns
-------
kg: torchkge.data_structures.KnowledgeGraph
kg_attr: torchkge.data_structures.KnowledgeGraph
"""
assert level in [4, 5]
if data_home is None:
data_home = get_data_home()
data_path = data_home + '/wikidatavitals-level{}'.format(level)
if not exists(data_path):
makedirs(data_path, exist_ok=True)
urlretrieve(
"https://graphs.telecom-paristech.fr/data/torchkge/kgs/wikidatavitals-level{}.zip"
.format(level),
data_home + '/wikidatavitals-level{}.zip'.format(level))
with zipfile.ZipFile(
data_home + '/wikidatavitals-level{}.zip'.format(level),
'r') as zip_ref:
zip_ref.extractall(data_home)
remove(data_home + '/wikidatavitals-level{}.zip'.format(level))
df = read_csv(data_path + '/edges.tsv',
sep='\t',
names=['from', 'to', 'rel'],
skiprows=1)
attributes = read_csv(data_path + '/attributes.tsv',
sep='\t',
names=['from', 'to', 'rel'],
skiprows=1)
entities = read_csv(data_path + '/entities.tsv', sep='\t')
relations = read_csv(data_path + '/relations.tsv', sep='\t')
nodes = read_csv(data_path + '/nodes.tsv', sep='\t')
df = enrich(df, entities, relations)
attributes = enrich(attributes, entities, relations)
relid2label = {
relations.loc[i, 'wikidataID']: relations.loc[i, 'label']
for i in relations.index
}
entid2label = {
entities.loc[i, 'wikidataID']: entities.loc[i, 'label']
for i in entities.index
}
entid2pagename = {
nodes.loc[i, 'wikidataID']: nodes.loc[i, 'pageName']
for i in nodes.index
}
kg = KnowledgeGraph(df)
kg_attr = KnowledgeGraph(attributes)
kg.relid2label = relid2label
kg_attr.relid2label = relid2label
kg.entid2label = entid2label
kg_attr.entid2label = entid2label
kg.entid2pagename = entid2pagename
kg_attr.entid2pagename = entid2pagename
return kg, kg_attr
def load_wikidatasets(which, limit_=0, data_home=None):
"""Load WikiDataSets dataset. See `here
<https://arxiv.org/abs/1906.04536>`__ for paper by Boschin et al.
originally presenting the dataset.
Parameters
----------
which: str
String indicating which subset of Wikidata should be loaded.
Available ones are `humans`, `companies`, `animals`, `countries` and
`films`.
limit_: int, optional (default=0)
This indicates a lower limit on the number of neighbors an entity
should have in the graph to be kept.
data_home: str, optional
Path to the `torchkge_data` directory (containing data folders). If
files are not present on disk in this directory, they are downloaded
and then placed in the right place.
Returns
-------
kg: torchkge.data_structures.KnowledgeGraph
"""
assert which in ['humans', 'companies', 'animals', 'countries', 'films']
if data_home is None:
data_home = get_data_home()
data_home = data_home + '/WikiDataSets'
data_path = data_home + '/' + which
if not exists(data_path):
print(f"Downloading WikiDataSets/{which}")
makedirs(data_path, exist_ok=True)
urlretrieve(
"https://graphs.telecom-paristech.fr/data/WikiDataSets/{}.tar.gz".
format(which), data_home + '/{}.tar.gz'.format(which))
with tarfile.open(data_home + '/{}.tar.gz'.format(which), 'r') as tf:
tf.extractall(data_home)
remove(data_home + '/{}.tar.gz'.format(which))
# add entity2idx, relation2idx
print(
f"Creating Knowledge Graph Data Structure using WikiDataSets/{which}")
df = read_csv(data_path + '/edges.tsv',
sep='\t',
names=['from', 'to', 'rel'],
skiprows=[0])
entities = read_csv(data_path + '/entities.tsv',
sep='\t',
names=['id', 'wid', 'label'],
skiprows=[0])
relations = read_csv(data_path + '/relations.tsv',
sep='\t',
names=['id', 'wid', 'label'],
skiprows=[0])
ix2ent = {i: e for i, e in zip(entities['id'], entities['label'])}
ix2rel = {i: r for i, r in zip(relations['id'], relations['label'])}
for i in range(len(df)):
h, t, r = df.loc[i]['from'], df.loc[i]['to'], df.loc[i]['rel']
df.loc[i] = [ix2ent[h], ix2ent[t], ix2rel[r]]
entities.drop_duplicates('label', inplace=True)
relations.drop_duplicates('label', inplace=True)
ent2ix = {e: i for i, e in enumerate(entities['label'])}
rel2ix = {r: i for i, r in enumerate(relations['label'])}
if limit_ > 0:
a = df.groupby('from').count()['rel']
b = df.groupby('to').count()['rel']
# Filter out nodes with too few facts
tmp = merge(
right=DataFrame(a).reset_index(),
left=DataFrame(b).reset_index(),
how='outer',
right_on='from',
left_on='to',
).fillna(0)
tmp['rel'] = tmp['rel_x'] + tmp['rel_y']
tmp = tmp.drop(['from', 'rel_x', 'rel_y'], axis=1)
tmp = tmp.loc[tmp['rel'] >= limit_]
df_bis = df.loc[df['from'].isin(tmp['to']) | df['to'].isin(tmp['to'])]
kg = KnowledgeGraph(df=df_bis, ent2ix=ent2ix, rel2ix=rel2ix)
else:
kg = KnowledgeGraph(df=df, ent2ix=ent2ix, rel2ix=rel2ix)
return kg
def load_fb13(data_home=None):
"""Load FB13 dataset.
Parameters
----------
data_home: str, optional
Path to the `torchkge_data` directory (containing data folders). If
files are not present on disk in this directory, they are downloaded
and then placed in the right place.
Returns
-------
kg_train: torchkge.data_structures.KnowledgeGraph
kg_val: torchkge.data_structures.KnowledgeGraph
kg_test: torchkge.data_structures.KnowledgeGraph
"""
if data_home is None:
data_home = get_data_home()
data_path = data_home + '/FB13'
if not exists(data_path):
makedirs(data_path, exist_ok=True)
urlretrieve(
"https://graphs.telecom-paristech.fr/data/torchkge/kgs/FB13.zip",
data_home + '/FB13.zip')
with zipfile.ZipFile(data_home + '/FB13.zip', 'r') as zip_ref:
zip_ref.extractall(data_home)
remove(data_home + '/FB13.zip')
df1 = read_csv(data_path + '/train2id.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'],
skiprows=[0])
df2 = read_csv(data_path + '/valid2id.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'],
skiprows=[0])
df3 = read_csv(data_path + '/test2id.txt',
sep='\t',
header=None,
names=['from', 'rel', 'to'],
skiprows=[0])
ent2idx = read_csv(data_path + '/entity2id.txt',
sep='\t',
header=None,
names=['entity', 'idx'],
skiprows=[0])
ent2idx = {e: i for e, i in zip(ent2idx['entity'], ent2idx['idx'])}
rel2idx = read_csv(data_path + '/relation2id.txt',
sep='\t',
header=None,
names=['relation', 'idx'],
skiprows=[0])
rel2idx = {r: i for r, i in zip(rel2idx['relation'], rel2idx['idx'])}
df = concat([df1, df2, df3])
kg = KnowledgeGraph(df=df, ent2ix=ent2idx, rel2ix=rel2idx)
return kg.split_kg(sizes=(len(df1), len(df2), len(df3)))
def setUp(self):
df = pd.DataFrame(
[[0, 1, 0], [0, 2, 0], [0, 3, 0], [0, 4, 0], [1, 2, 1], [1, 3, 2],
[2, 4, 0], [3, 4, 4], [5, 4, 0]],
columns=['from', 'to', 'rel'])
self.kg = KnowledgeGraph(df)
def test_get_hpt(self):
kg = KnowledgeGraph(df=self.df)
t = cat((kg.head_idx.view(-1, 1), kg.tail_idx.view(
-1, 1), kg.relations.view(-1, 1)),
dim=1)
assert get_hpt(t) == {0: 1.5, 1: 1., 2: 1., 3: 1.}
def load_wikidatasets(which, limit_=None, data_home=None):
"""Load WikiDataSets dataset. See `here
<https://arxiv.org/abs/1906.04536>`__ for paper by Boschin et al.
originally presenting the dataset.
Parameters
----------
which: str
String indicating which subset of Wikidata should be loaded.
Available ones are `humans`, `companies`, `animals`, `countries` and
`films`.
limit_: int, optional (default=0)
This indicates a lower limit on the number of neighbors an entity
should have in the graph to be kept.
data_home: str, optional
Path to the `torchkge_data` directory (containing data folders). If
files are not present on disk in this directory, they are downloaded
and then placed in the right place.
Returns
-------
kg_train: torchkge.data_structures.KnowledgeGraph
kg_val: torchkge.data_structures.KnowledgeGraph
kg_test: torchkge.data_structures.KnowledgeGraph
"""
assert which in ['humans', 'companies', 'animals', 'countries', 'films']
if data_home is None:
data_home = get_data_home()
data_home = data_home + '/WikiDataSets'
data_path = data_home + '/' + which
if not exists(data_path):
makedirs(data_path, exist_ok=True)
urlretrieve(
"https://graphs.telecom-paristech.fr/WikiDataSets/{}.tar.gz".
format(which), data_home + '/{}.tar.gz'.format(which))
with tarfile.open(data_home + '/{}.tar.gz'.format(which), 'r') as tf:
tf.extractall(data_home)
remove(data_home + '/{}.tar.gz'.format(which))
df = read_csv(data_path + '/edges.txt'.format(which),
sep='\t',
header=1,
names=['from', 'to', 'rel'])
a = df.groupby('from').count()['rel']
b = df.groupby('to').count()['rel']
# Filter out nodes with too few facts
tmp = merge(
right=DataFrame(a).reset_index(),
left=DataFrame(b).reset_index(),
how='outer',
right_on='from',
left_on='to',
).fillna(0)
tmp['rel'] = tmp['rel_x'] + tmp['rel_y']
tmp = tmp.drop(['from', 'rel_x', 'rel_y'], axis=1)
tmp = tmp.loc[tmp['rel'] >= limit_]
df_bis = df.loc[df['from'].isin(tmp['to']) | df['to'].isin(tmp['to'])]
kg = KnowledgeGraph(df_bis)
kg_train, kg_val, kg_test = kg.split_kg(share=0.8, validation=True)
return kg_train, kg_val, kg_test
