def test_output_sizes():
''' Test to check whether embedding matrix sizes match the input data (num rel/ent and k)
'''
def perform_test():
X = load_wn18rr()
k = 5
unique_entities = np.unique(
np.concatenate([X['train'][:, 0], X['train'][:, 2]], 0))
unique_relations = np.unique(X['train'][:, 1])
model = TransE(batches_count=100,
seed=555,
epochs=1,
k=k,
loss='multiclass_nll',
loss_params={'margin': 5},
verbose=True,
optimizer='sgd',
optimizer_params={'lr': 0.001})
model.fit(X['train'])
# verify ent and rel shapes
assert (model.trained_model_params[0].shape[0] == len(unique_entities))
assert (
model.trained_model_params[1].shape[0] == len(unique_relations))
# verify k
assert (model.trained_model_params[0].shape[1] == k)
assert (model.trained_model_params[1].shape[1] == k)
# Normal mode
perform_test()
# Large graph mode
set_entity_threshold(10)
perform_test()
reset_entity_threshold()
def test_evaluate_with_ent_subset_large_graph():
set_entity_threshold(1)
X = load_wn18()
model = ComplEx(batches_count=10, seed=0, epochs=2, k=10, eta=1,
optimizer='sgd', optimizer_params={'lr': 1e-5},
loss='pairwise', loss_params={'margin': 0.5},
regularizer='LP', regularizer_params={'p': 2, 'lambda': 1e-5},
verbose=True)
model.fit(X['train'])
X_filter = np.concatenate((X['train'], X['valid'], X['test']))
all_nodes = set(X_filter[:, 0]).union(X_filter[:, 2])
entities_subset = np.random.choice(list(all_nodes), 100, replace=False)
ranks = evaluate_performance(X['test'][::10],
model=model,
filter_triples=X_filter,
corrupt_side='o',
use_default_protocol=False,
entities_subset=list(entities_subset),
verbose=True)
assert np.sum(ranks > (100 + 1)) == 0, "No ranks must be greater than 101"
reset_entity_threshold()
def test_large_graph_mode_adam():
set_entity_threshold(10)
X = load_wn18()
model = ComplEx(batches_count=100,
seed=555,
epochs=1,
k=50,
loss='multiclass_nll',
loss_params={'margin': 5},
verbose=True,
optimizer='adam',
optimizer_params={'lr': 0.001})
try:
model.fit(X['train'])
except Exception as e:
print(str(e))
reset_entity_threshold()
def test_large_graph_mode():
set_entity_threshold(10)
X = load_wn18()
model = ComplEx(batches_count=100,
seed=555,
epochs=1,
k=50,
loss='multiclass_nll',
loss_params={'margin': 5},
verbose=True,
optimizer='sgd',
optimizer_params={'lr': 0.001})
model.fit(X['train'])
X_filter = np.concatenate((X['train'], X['valid'], X['test']), axis=0)
evaluate_performance(X['test'][::1000],
model,
X_filter,
verbose=True,
corrupt_side='s,o')
y = model.predict(X['test'][:1])
print(y)
reset_entity_threshold()
from ampligraph.latent_features import set_entity_threshold
from excut.explanations_mining.explaining_engines_extended import PathBasedClustersExplainerExtended
from excut.explanations_mining.simple_miner.description_miner_extended import ExplanationStructure
from excut.feedback import strategies
from excut.kg.kg_query_interface_extended import RdflibKGQueryInterfaceExtended, EndPointKGQueryInterfaceExtended
from excut.kg.kg_slicing import KGSlicer
from excut.utils.output_utils import write_triples
set_entity_threshold(10e6)
# set_entity_threshold(10e4)s
import json
import time
import os, argparse
from excut.utils.logging import logger
from rdflib import Graph
from excut.pipeline.explainable_clustering import ExClusteringImpl
from excut.kg.kg_indexing import Indexer
from excut.kg.utils.Constants import DEFUALT_AUX_RELATION
from excut.kg.kg_triples_source import FileTriplesSource
from excut.clustering.target_entities import EntitiesLabelsFile
from excut.embedding.embedding_adapters import AmpligraphEmbeddingAdapter, UpdateDataMode, \
UpdateMode
def initiate_problem(args, time_now):
# objective quality
objective_quality_measure = args.objective_quality