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.KnowledgeGraph`
kg_val: `torchkge.data.KnowledgeGraph`
kg_test: `torchkge.data.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/datasets/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 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.KnowledgeGraph`
kg_val: `torchkge.data.KnowledgeGraph`
kg_test: `torchkge.data.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_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.KnowledgeGraph`
kg_val: `torchkge.data.KnowledgeGraph`
kg_test: `torchkge.data.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/datasets/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 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.KnowledgeGraph`
kg_val: `torchkge.data.KnowledgeGraph`
kg_test: `torchkge.data.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