def setUp(self):
self.id = False
self.uv = False
self.ub = False
self.threshold = 0.8
self.assoc_threshold = 0.3
seed = 10
dim = 512
prop = 0.1
input_dir, output_dir = tools.read_config()
relation_symbols = symbol_definitions.uni_relation_symbols()
vector_factory = VectorFactory(seed)
isA_symbols = symbol_definitions.isA_symbols()
sentence_symbols = symbol_definitions.sentence_role_symbols()
self.corpus_dict, self.id_vectors, self.semantic_pointers = \
tools.setup_corpus(
input_dir, relation_symbols, dim, vector_factory,
seed, self.id, self.uv, prop)
self.createAssociator(self.id_vectors, self.semantic_pointers)
self.tester = WordnetAssociativeMemoryTester(
self.corpus_dict, self.id_vectors, self.semantic_pointers,
relation_symbols, self.associator, seed, output_dir, isA_symbols,
sentence_symbols, VectorFactory(), self.uv, True)
def setUp(self):
self.id = False
self.uv = False
self.ub = False
self.threshold = 0.8
self.assoc_threshold = 0.3
seed = 10
dim = 512
prop = 0.1
input_dir, output_dir = tools.read_config()
relation_symbols = symbol_definitions.uni_relation_symbols()
vector_factory = VectorFactory(seed)
isA_symbols = symbol_definitions.isA_symbols()
sentence_symbols = symbol_definitions.sentence_role_symbols()
self.corpus_dict, self.id_vectors, self.semantic_pointers = \
tools.setup_corpus(
input_dir, relation_symbols, dim, vector_factory,
seed, self.id, self.uv, prop)
self.createAssociator(self.id_vectors, self.semantic_pointers)
self.tester = WordnetAssociativeMemoryTester(
self.corpus_dict, self.id_vectors, self.semantic_pointers,
relation_symbols, self.associator, seed, output_dir, isA_symbols,
sentence_symbols, VectorFactory(), self.uv, True)
def run(num_runs, jump_trials, hier_trials, sent_trials, deep_trials, expr,
unitary_roles, short_sentence, do_neg, corpus_seed,
extractor_seed, test_seed, seed, dimension, num_synsets,
proportion, unitary_relations, id_vecs, sp_noise, normalize,
abstract, synapse, timesteps, threshold, probe_all, identical,
fast, plot, gpus, name):
input_dir, output_dir = read_config()
if not name:
name = "hrr_scaling_results"
directory = make_filename(name, output_dir)
if not os.path.isdir(directory):
os.makedirs(directory)
make_sym_link(
os.path.split(directory)[1], os.path.join(output_dir, 'latest'))
neural = not abstract
if gpus is not None:
gpus.sort()
def make_corpus_factory(dimension, input_dir, unitary_relations, id_vecs,
proportion, num_synsets, sp_noise, normalize):
def make_corpus():
corpus = VectorizedCorpus(
dimension=dimension, input_dir=input_dir,
unitary_relations=unitary_relations, id_vecs=id_vecs,
sp_noise=sp_noise, normalize=normalize,
num_synsets=num_synsets, proportion=proportion)
return corpus
return make_corpus
def make_extractor_factory(neural, fast, gpus, plot, threshold,
timesteps, synapse):
# pick an extraction algorithm
def make_extractor(id_vectors, semantic_pointers,
probe_keys, output_dir):
if neural:
if fast and gpus:
extractor = FastNeuralExtractor(
id_vectors, semantic_pointers,
threshold=threshold, probe_keys=probe_keys,
timesteps=timesteps, synapse=synapse,
plot=plot, gpus=gpus, identical=identical,
output_dir=output_dir)
else:
extractor = NeuralExtractor(
id_vectors, semantic_pointers, threshold=threshold,
probe_keys=probe_keys, timesteps=timesteps,
synapse=synapse, plot=plot, gpus=gpus,
identical=identical, output_dir=output_dir)
else:
extractor = Extractor(
id_vectors, semantic_pointers,
threshold, output_dir=output_dir)
return extractor
return make_extractor
corpus_factory = make_corpus_factory(
dimension, input_dir, unitary_relations, id_vecs,
proportion, num_synsets, sp_noise, normalize)
extractor_factory = make_extractor_factory(
neural, fast, gpus, plot, threshold, timesteps, synapse)
if seed != -1:
random.seed(seed)
corpus_seed = random.randrange(1000)
extractor_seed = random.randrange(1000)
test_seed = random.randrange(1000)
else:
if corpus_seed == -1:
corpus_seed = random.randrange(1000)
if extractor_seed == -1:
extractor_seed = random.randrange(1000)
if test_seed == -1:
test_seed = random.randrange(1000)
np.random.seed(test_seed)
random.seed(test_seed)
test_runner = ExtractionTester(
corpus_factory, extractor_factory, corpus_seed,
extractor_seed, test_seed, probe_all, directory)
if jump_trials > 0:
test = JumpTest(jump_trials)
test_runner.add_test(test)
if hier_trials > 0:
test = HierarchicalTest(hier_trials, do_neg=do_neg)
test_runner.add_test(test)
if sent_trials > 0:
test = SentenceTest(
sent_trials, deep=False,
unitary=unitary_roles, short=short_sentence)
test_runner.add_test(test)
if deep_trials > 0:
test = SentenceTest(
deep_trials, deep=True,
unitary=unitary_roles, short=short_sentence)
test_runner.add_test(test)
if expr:
expr_trials = expr[0]
expr = expr[1]
test = ExpressionTest(expr_trials, expression=expr)
test_runner.add_test(test)
test_runner.run_bootstrap(num_runs)
def run(num_runs, jump_trials, hier_trials, sent_trials, deep_trials, expr,
unitary_roles, short_sentence, do_neg, corpus_seed, extractor_seed,
test_seed, seed, dimension, num_synsets, proportion, unitary_relations,
id_vecs, sp_noise, normalize, abstract, synapse, timesteps, threshold,
probe_all, identical, fast, plot, gpus, name):
input_dir, output_dir = read_config()
if not name:
name = "hrr_scaling_results"
directory = make_filename(name, output_dir)
if not os.path.isdir(directory):
os.makedirs(directory)
make_sym_link(
os.path.split(directory)[1], os.path.join(output_dir, 'latest'))
neural = not abstract
if gpus is not None:
gpus.sort()
def make_corpus_factory(dimension, input_dir, unitary_relations, id_vecs,
proportion, num_synsets, sp_noise, normalize):
def make_corpus():
corpus = VectorizedCorpus(dimension=dimension,
input_dir=input_dir,
unitary_relations=unitary_relations,
id_vecs=id_vecs,
sp_noise=sp_noise,
normalize=normalize,
num_synsets=num_synsets,
proportion=proportion)
return corpus
return make_corpus
def make_extractor_factory(neural, fast, gpus, plot, threshold, timesteps,
synapse):
# pick an extraction algorithm
def make_extractor(id_vectors, semantic_pointers, probe_keys,
output_dir):
if neural:
if fast and gpus:
extractor = FastNeuralExtractor(id_vectors,
semantic_pointers,
threshold=threshold,
probe_keys=probe_keys,
timesteps=timesteps,
synapse=synapse,
plot=plot,
gpus=gpus,
identical=identical,
output_dir=output_dir)
else:
extractor = NeuralExtractor(id_vectors,
semantic_pointers,
threshold=threshold,
probe_keys=probe_keys,
timesteps=timesteps,
synapse=synapse,
plot=plot,
gpus=gpus,
identical=identical,
output_dir=output_dir)
else:
extractor = Extractor(id_vectors,
semantic_pointers,
threshold,
output_dir=output_dir)
return extractor
return make_extractor
corpus_factory = make_corpus_factory(dimension, input_dir,
unitary_relations, id_vecs,
proportion, num_synsets, sp_noise,
normalize)
extractor_factory = make_extractor_factory(neural, fast, gpus, plot,
threshold, timesteps, synapse)
if seed != -1:
random.seed(seed)
corpus_seed = random.randrange(1000)
extractor_seed = random.randrange(1000)
test_seed = random.randrange(1000)
else:
if corpus_seed == -1:
corpus_seed = random.randrange(1000)
if extractor_seed == -1:
extractor_seed = random.randrange(1000)
if test_seed == -1:
test_seed = random.randrange(1000)
np.random.seed(test_seed)
random.seed(test_seed)
test_runner = ExtractionTester(corpus_factory, extractor_factory,
corpus_seed, extractor_seed, test_seed,
probe_all, directory)
if jump_trials > 0:
test = JumpTest(jump_trials)
test_runner.add_test(test)
if hier_trials > 0:
test = HierarchicalTest(hier_trials, do_neg=do_neg)
test_runner.add_test(test)
if sent_trials > 0:
test = SentenceTest(sent_trials,
deep=False,
unitary=unitary_roles,
short=short_sentence)
test_runner.add_test(test)
if deep_trials > 0:
test = SentenceTest(deep_trials,
deep=True,
unitary=unitary_roles,
short=short_sentence)
test_runner.add_test(test)
if expr:
expr_trials = expr[0]
expr = expr[1]
test = ExpressionTest(expr_trials, expression=expr)
test_runner.add_test(test)
test_runner.run_bootstrap(num_runs)
