def test_train_test_split_fast():
X = load_fb15k_237()
x_all = np.concatenate([X['train'], X['valid'], X['test']], 0)
unique_entities = len(set(x_all[:, 0]).union(x_all[:, 2]))
unique_rels = len(set(x_all[:, 1]))
x_train, x_test = train_test_split_no_unseen(x_all, 0.90)
assert x_train.shape[0] + x_test.shape[0] == x_all.shape[0]
unique_entities_train = len(set(x_train[:, 0]).union(x_train[:, 2]))
unique_rels_train = len(set(x_train[:, 1]))
assert unique_entities_train == unique_entities and unique_rels_train == unique_rels
with pytest.raises(Exception) as e:
x_train, x_test = train_test_split_no_unseen(x_all, 0.99, allow_duplication=False)
assert str(e.value) == "Cannot create a test split of the desired size. " \
"Some entities will not occur in both training and test set. " \
"Set allow_duplication=True," \
"remove filter on test predicates or " \
"set test_size to a smaller value."
x_train, x_test = train_test_split_no_unseen(x_all, 0.99, allow_duplication=True)
assert x_train.shape[0] + x_test.shape[0] > x_all.shape[0]
unique_entities_train = len(set(x_train[:, 0]).union(x_train[:, 2]))
unique_rels_train = len(set(x_train[:, 1]))
assert unique_entities_train == unique_entities and unique_rels_train == unique_rels
def test_load_fb15k_237():
fb15k_237 = load_fb15k_237()
assert len(fb15k_237['train']) == 272115
# - 9 because 9 triples containing unseen entities are removed
assert len(fb15k_237['valid']) == 17535 - 9
# - 28 because 28 triples containing unseen entities are removed
assert len(fb15k_237['test']) == 20466 - 28
def main():
# load Wordnet18 dataset:
# X = load_wn18()
X = load_fb15k_237()
modify_flag = False
# Initialize a ComplEx neural embedding model with pairwise loss function:
# The model will be trained for 300 epochs.
model = ComplEx(
batches_count=10,
seed=0,
epochs=30,
k=150,
eta=10,
# Use adam optimizer with learning rate 1e-3
optimizer='adam',
optimizer_params={'lr': 1e-3},
# Use pairwise loss with margin 0.5
loss='pairwise',
loss_params={'margin': 0.5},
# Use L2 regularizer with regularizer weight 1e-5
regularizer='LP',
regularizer_params={
'p': 2,
'lambda': 1e-5
},
# Enable stdout messages (set to false if you don't want to display)
verbose=True) #, modify_flag = modify_flag)
if False:
# ground truth params (have not tried yet)
# k: 350; epochs: 4000; eta: 30; loss: self_adversarial; loss_params: alpha: 1; margin: 0.5; optimizer: adam; optimizer_params: lr: 0.0001; seed: 0; batches_count: 50
model = ComplEx(
batches_count=50,
seed=0,
epochs=4000,
k=350,
eta=30,
# Use adam optimizer with learning rate 1e-3
optimizer='adam',
optimizer_params={'lr': 1e-4},
# Use pairwise loss with margin 0.5
loss='self_adversarial',
loss_params={
'margin': 0.5,
'alpha': 1
},
# Use L2 regularizer with regularizer weight 1e-5
regularizer='LP',
regularizer_params={
'p': 2,
'lambda': 1e-5
},
# Enable stdout messages (set to false if you don't want to display)
verbose=True,
modify_flag=modify_flag)
# For evaluation, we can use a filter which would be used to filter out
# positives statements created by the corruption procedure.
# Here we define the filter set by concatenating all the positives
filter = np.concatenate((X['train'], X['valid'], X['test']))
# Fit the model on training and validation set
model.fit(X['train'],
early_stopping=True,
early_stopping_params= \
{
'x_valid': X['valid'], # validation set
'criteria': 'hits10', # Uses hits10 criteria for early stopping
'burn_in': 100, # early stopping kicks in after 100 epochs
'check_interval': 20, # validates every 20th epoch
'stop_interval': 5, # stops if 5 successive validation checks are bad.
'x_filter': filter, # Use filter for filtering out positives
'corruption_entities': 'all', # corrupt using all entities
'corrupt_side': 's+o' # corrupt subject and object (but not at once)
}
)
# Run the evaluation procedure on the test set (with filtering).
# To disable filtering: filter_triples=None
# Usually, we corrupt subject and object sides separately and compute ranks
# restore model
sys.exit()
# import the inspect_checkpoint library
sys.exit()
ranks = evaluate_performance(
X['test'],
model=model,
filter_triples=filter,
use_default_protocol=
True, # corrupt subj and obj separately while evaluating
verbose=True)
# compute and print metrics:
mrr = mrr_score(ranks)
hits_10 = hits_at_n_score(ranks, n=10)
print("MRR: %f, Hits@10: %f" % (mrr, hits_10))
