def extract(self, item, query, target_keys=None):
if target_keys:
self.print_instance_difficulty(item, query)
item_hrr = HRR(data=item)
query_hrr = HRR(data=query)
noisy_hrr = item_hrr.convolve(~query_hrr)
return self.associate(noisy_hrr.v, target_keys)
def extract(self, item, query, target_keys=None):
if target_keys:
self.print_instance_difficulty(item, query)
item_hrr = HRR(data=item)
query_hrr = HRR(data=query)
noisy_hrr = item_hrr.convolve(~query_hrr)
return self.associate(noisy_hrr.v, target_keys)
def get_stats(self, clean_result_vector, goal, other_answers, fp):
size = np.linalg.norm(clean_result_vector)
if not goal:
comparisons = self.find_matches(clean_result_vector,
self.semantic_pointers)
largest_match = max(comparisons, key=lambda x: x[1])
return (largest_match[0], largest_match[1], size)
else:
comparisons = self.find_matches(clean_result_vector,
self.semantic_pointers,
exempt=[goal])
if other_answers:
invalids = []
valids = []
for c in comparisons:
if c[0] in other_answers:
valids.append(c)
else:
invalids.append(c)
max_invalid = max(invalids, key=lambda x: x[1])
max_invalid_key, max_invalid_match = max_invalid
if len(valids) == 0:
second_key, second_match = max_invalid
else:
max_valid = max(valids, key=lambda x: x[1])
max_valid_key, max_valid_match = max_valid
if max_invalid_match > max_valid_match:
second_key, second_match = max_invalid
else:
second_key, second_match = max_valid
else:
second_key, second_match = max(comparisons, key=lambda x: x[1])
max_invalid_match = second_match
hrr_vec = HRR(data=self.semantic_pointers[goal])
target_match = hrr_vec.compare(HRR(data=clean_result_vector))
if target_match > second_match:
clean_result = goal
else:
clean_result = second_key
return (clean_result, target_match, second_match, size,
max_invalid_match)
def get_stats(self, clean_result_vector, goal, other_answers, fp):
size = np.linalg.norm(clean_result_vector)
if not goal:
comparisons = self.find_matches(clean_result_vector,
self.semantic_pointers)
largest_match = max(comparisons, key=lambda x: x[1])
return (largest_match[0], largest_match[1], size)
else:
comparisons = self.find_matches(
clean_result_vector, self.semantic_pointers, exempt=[goal])
if other_answers:
invalids = []
valids = []
for c in comparisons:
if c[0] in other_answers:
valids.append(c)
else:
invalids.append(c)
max_invalid = max(invalids, key=lambda x: x[1])
max_invalid_key, max_invalid_match = max_invalid
if len(valids) == 0:
second_key, second_match = max_invalid
else:
max_valid = max(valids, key=lambda x: x[1])
max_valid_key, max_valid_match = max_valid
if max_invalid_match > max_valid_match:
second_key, second_match = max_invalid
else:
second_key, second_match = max_valid
else:
second_key, second_match = max(comparisons, key=lambda x: x[1])
max_invalid_match = second_match
hrr_vec = HRR(data=self.semantic_pointers[goal])
target_match = hrr_vec.compare(HRR(data=clean_result_vector))
if target_match > second_match:
clean_result = goal
else:
clean_result = second_key
return (clean_result, target_match,
second_match, size, max_invalid_match)
def create_role_hrrs(self):
role_hrrs = {}
for role in self.sentence_symbols:
role_hrrs[role] = HRR(self.dimension)
if self.unitary:
role_hrrs[role].make_unitary()
return role_hrrs
def run(self):
expression = self.expression
dimension = len(self.id_vectors.values()[0])
expression = expression.replace('!id', 'p0')
num_ids = expression.count('id')
expression = expression.replace('id', '%s')
temp_names = ['id' + str(i) for i in range(num_ids)]
expression = expression % tuple(temp_names)
chosen_id_keys = self.rng.sample(self.id_vectors,
expression.count('id') + 1)
chosen_id_vectors = [
HRR(data=self.id_vectors[key]) for key in chosen_id_keys
]
target_key = chosen_id_keys[0]
names_dict = dict(zip(['p0'] + temp_names, chosen_id_vectors))
names_keys_dict = dict(zip(['p0'] + temp_names, chosen_id_keys))
query_vectors = nf.find_query_vectors(expression, 'p0')
query_expression = '*'.join(query_vectors)
temp_names = expression.replace('*', '+').split('+')
temp_names = [tn.strip() for tn in temp_names]
unitary_names = [u for u in temp_names if u[-1:] == "u"]
vocab = Vocabulary(dimension, unitary=unitary_names)
for n, v in names_dict.iteritems():
vocab.add(n, v)
print "expression:", expression
print "query_expression:", query_expression
print "unitary_names:", unitary_names
print "target_key:", target_key
print "name_keys_dict:", names_keys_dict
test_vector = eval(expression, {}, vocab)
test_vector.normalize()
query_vector = eval(query_expression, {}, vocab)
result, correct, valid, exact = self.test_link(query_vector.v,
test_vector.v,
None,
target_key,
self.output_file,
return_vec=False,
answers=[target_key])
def print_instance_difficulty(self, item, query, target_keys):
if target_keys:
# Print data about how difficult the current instance is
correct_key = target_keys[0]
item_hrr = HRR(data=item)
query_hrr = HRR(data=query)
noisy_hrr = item_hrr.convolve(~query_hrr)
correct_hrr = HRR(data=self.index_vectors[correct_key])
sim = noisy_hrr.compare(correct_hrr)
dot = np.dot(noisy_hrr.v, correct_hrr.v)
norm = np.linalg.norm(noisy_hrr.v)
print "Statistics when extraction computed exactly:"
print ("Cosine Similarity between extracted vector "
"(before assoc) and correct index vector: "), sim
print ("Dot product between extracted vector (before assoc) "
"and correct index vector: "), dot
print "Norm of extracted vector (before assoc): ", norm
self.ideal_dot = dot
hrrs = [(key, HRR(data=iv))
for key, iv in self.index_vectors.iteritems()
if key != correct_key]
sims = [noisy_hrr.compare(h) for (k, h) in hrrs]
dots = [np.dot(noisy_hrr.v, h.v) for (k, h) in hrrs]
sim = max(sims)
dot = max(dots)
print "Cosine Similarity of closest incorrect index vector ", sim
print "Dot product of closest incorrect index vector ", dot
self.second_dot = dot
def __init__(self,
index_vectors,
stored_vectors,
bootstrapper,
threshold=0.3,
output_dir='.'):
self.index_vectors = index_vectors
self.stored_vectors = stored_vectors
self.threshold = threshold
self.dimension = len(index_vectors.values()[0])
self.num_items = len(index_vectors)
self.hrr_vecs = collections.OrderedDict([
(key, HRR(data=self.index_vectors[key]))
for key in self.index_vectors
])
self.similarities = collections.OrderedDict(
zip(self.index_vectors, [0 for i in range(len(index_vectors))]))
self.return_vec = True
self.bootstrapper = bootstrapper
self.output_dir = output_dir
def print_instance_difficulty(self, item, query, target_keys):
if target_keys:
# Print data about how difficult the current instance is
correct_key = target_keys[0]
item_hrr = HRR(data=item)
query_hrr = HRR(data=query)
noisy_hrr = item_hrr.convolve(~query_hrr)
correct_hrr = HRR(data=self.index_vectors[correct_key])
sim = noisy_hrr.compare(correct_hrr)
dot = np.dot(noisy_hrr.v, correct_hrr.v)
norm = np.linalg.norm(noisy_hrr.v)
print "Statistics when extraction computed exactly:"
print(
"Cosine Similarity between extracted vector "
"(before assoc) and correct index vector: "), sim
print(
"Dot product between extracted vector (before assoc) "
"and correct index vector: "), dot
print "Norm of extracted vector (before assoc): ", norm
self.ideal_dot = dot
hrrs = [(key, HRR(data=iv))
for key, iv in self.index_vectors.iteritems()
if key != correct_key]
sims = [noisy_hrr.compare(h) for (k, h) in hrrs]
dots = [np.dot(noisy_hrr.v, h.v) for (k, h) in hrrs]
sim = max(sims)
dot = max(dots)
print "Cosine Similarity of closest incorrect index vector ", sim
print "Dot product of closest incorrect index vector ", dot
self.second_dot = dot
def findAllParents(self,
start_key,
target_key=None,
rtype=[],
use_HRR=False,
print_output=False):
if print_output:
print >> self.output_file, \
"In find all parents, useHRR=", use_HRR
print >> self.output_file, "Start:", start_key
if target_key is not None:
print >> self.output_file, "Target:", target_key
use_vecs = use_HRR and self.extractor.return_vec
level = 0
if use_vecs:
layerA = [self.semantic_pointers[start_key]]
if target_key:
target_vector = self.semantic_pointers[target_key]
target_hrr = HRR(data=target_vector)
else:
layerA = [start_key]
layerB = []
parents = set()
while len(layerA) > 0:
word = layerA.pop()
# test whether we've found the target
found = False
if use_vecs:
word_hrr = HRR(data=word)
found = target_hrr.compare(word_hrr) > self.decision_threshold
else:
found = word == target_key
if found:
if print_output:
print >> self.output_file, target_key, \
"found at level ", level
return level
if use_vecs:
key = self.get_key_from_vector(word, self.semantic_pointers)
else:
key = word
if key:
if key in parents:
continue
if level > 0:
parents.add(key)
if print_output:
print >> self.output_file, key, \
"found at level ", level
links = []
if not use_HRR:
links = [
r[1] for r in self.corpus_dict[word] if r[0] in rtype
]
else:
for symbol in rtype:
answers = [
r[1] for r in self.corpus_dict[key]
if r[0] == symbol
]
relation_vec = self.relation_type_vectors[symbol]
if len(answers) == 0:
target = None
else:
target = answers[0]
relations = filter(
lambda x: x[0] in self.relation_type_vectors,
self.corpus_dict[key])
num_relations = len(relations)
if use_vecs:
result = self.test_link(
relation_vec,
word,
key,
target,
self.output_file,
return_vec=True,
depth=level,
num_relations=num_relations,
answers=answers)
links.append(result)
else:
results = self.test_link(
relation_vec,
None,
key,
target,
self.output_file,
return_vec=False,
depth=level,
num_relations=num_relations,
answers=answers)
if answers:
results = results[0]
links.extend(results)
if len(links) > 0:
layerB.extend(links)
if len(layerA) == 0:
level = level + 1
layerA = layerB
layerB = []
if target_key is None:
return list(parents)
else:
return -1
def plot_simulation(self, target_keys):
then = datetime.datetime.now()
correct_key = None
if target_keys:
correct_key = target_keys[0]
sim = self.simulator
t = sim.trange()
max_val = 5.0 / np.sqrt(self.dimension)
gs = gridspec.GridSpec(7, 2)
fig = plt.figure(figsize=(10, 10))
ax = plt.subplot(gs[0, 0])
plt.plot(t, self.data[self.D_probe], label='D')
title = 'Input to associative memory'
ax.text(.01, 1.20, title, horizontalalignment='left',
transform=ax.transAxes)
plt.ylim((-max_val, max_val))
ax = plt.subplot(gs[0, 1])
plt.plot(t, self.data[self.output_probe], label='Output')
title = 'Output of associative memory'
ax.text(.01, 1.20, title, horizontalalignment='left',
transform=ax.transAxes)
plt.ylim((-max_val, max_val))
ax = plt.subplot(gs[1:3, :])
if len(self.index_vectors) < 1000:
for key, v in self.index_vectors.iteritems():
input_sims = np.dot(self.data[self.D_probe], v)
label = str(key[1])
if key == correct_key:
plt.plot(t, input_sims, '--', label=label + '*')
else:
plt.plot(t, input_sims, label=label)
title = (
'Dot product between id vectors and input to assoc memory.\n'
'Target %s is dashed line.' % str(correct_key))
ax.text(.01, 0.80, title, horizontalalignment='left',
transform=ax.transAxes)
# plt.legend(bbox_to_anchor=(-0.03, 0.5), loc='center right')
if self.ideal_dot:
ax.text(.01, 0.10, "Ideal dot: " + str(self.ideal_dot),
horizontalalignment='left', transform=ax.transAxes)
if self.second_dot:
ax.text(.99, 0.10, "Second dot: " + str(self.second_dot),
horizontalalignment='right', transform=ax.transAxes)
plt.ylim((-1.0, 1.5))
plt.axhline(1.0, ls=':', c='k')
ax = plt.subplot(gs[3:5, :])
for key, p in self.assoc_probes.iteritems():
if key == correct_key:
plt.plot(t, self.data[p], '--')
else:
plt.plot(t, self.data[p])
title = (
'Decoded values of association populations.\n' +
'Target %s is dashed line.' % str(correct_key))
ax.text(.01, 0.80, title, horizontalalignment='left',
transform=ax.transAxes)
plt.ylim((-0.2, 1.5))
plt.axhline(y=1.0, ls=':', c='k')
ax = plt.subplot(gs[5:7, :])
before_ls = '--'
after_ls = '-'
before_norms = [np.linalg.norm(v) for v in self.data[self.D_probe]]
after_norms = [np.linalg.norm(v) for v in self.data[self.output_probe]]
plt.plot(t, before_norms, before_ls, c='g', label='Norm - Before')
plt.plot(t, after_norms, after_ls, c='g', label='Norm - After')
if correct_key is not None:
correct_index_hrr = HRR(data=self.index_vectors[correct_key])
correct_stored_hrr = HRR(data=self.stored_vectors[correct_key])
before_sims = [correct_index_hrr.compare(HRR(data=i))
for i in self.data[self.D_probe]]
after_sims = [correct_stored_hrr.compare(HRR(data=o))
for o in self.data[self.output_probe]]
plt.plot(t, before_sims, before_ls, c='b',
label='Cosine Sim - Before')
plt.plot(t, after_sims, after_ls, c='b',
label='Cosine Sim - After')
title = 'Before and After Associative Memory'
ax.text(.01, 0.90, title, horizontalalignment='left',
transform=ax.transAxes)
plt.ylim((-1.0, 1.5))
plt.legend(loc=4, prop={'size': 6})
plt.axhline(y=1.0, ls=':', c='k')
ax.set_xlabel('Time (s)')
date_time_string = str(datetime.datetime.now()).split('.')[0]
date_time_string = reduce(lambda y, z: string.replace(y, z, "_"),
[date_time_string, ":", ".", " ", "-"])
plot_name = 'neural_extraction_' + date_time_string + ".png"
plot_path = os.path.join(self.output_dir, plot_name)
plt.savefig(plot_path)
symlink_name = os.path.join(
self.output_dir, 'latest_neural_extraction')
make_sym_link(plot_name, symlink_name)
now = datetime.datetime.now()
self.write_to_runtime_file(now - then, "plot")
plt.close(fig)
def form_knowledge_base(self, id_vecs=True, unitary=False):
# Check existence of corpus
if self.corpus_dict is None:
raise Exception("Attempted to form the knowledge "
"base without a corpus.")
print "Number of items in knowledge base:", len(self.corpus_dict)
if not id_vecs:
print "Processing Corpus"
self.processCorpus()
print "Generating relation type vectors"
print "Using relation types: ", self.relation_symbols
self.relation_type_vectors = {symbol: HRR(self.dimension)
for symbol in self.relation_symbols}
if unitary:
for k, h in self.relation_type_vectors.iteritems():
h.make_unitary()
if id_vecs:
key_order = self.corpus_dict.keys()
else:
# Order words by the dependencies of their definitions
# Only have to do this if we're not using ID-vectors
key_order = []
resolved = set(self.relation_symbols)
dependencies = {}
for key in self.corpus_dict.keys():
dependencies[key] = set(
[tag[1] for tag in self.corpus_dict[key]
if tag[0] in self.relation_symbols])
while len(key_order) < (len(self.corpus_dict)
+ len(self.relation_symbols)):
resolvable = set()
for key in dependencies:
if dependencies[key].issubset(resolved):
resolvable.add(key)
# add the resolvable keys to the order list and resolved set
key_order.extend(resolvable)
resolved = resolved.union(resolvable)
# remove resolved tags from the dependency dictionary
for r in resolvable:
del dependencies[r]
# if no items are resolvable, we're stuck
if len(resolvable) == 0:
break
del resolved
del resolvable
if len(key_order) < len(self.corpus_dict):
raise Exception("Dependency resolution failed.")
self.semantic_pointers = collections.OrderedDict()
print "Generating ID-vectors"
if id_vecs:
self.id_vectors = collections.OrderedDict()
for key in key_order:
self.id_vectors[key] = HRR(self.dimension)
else:
self.id_vectors = self.semantic_pointers
print "Generating HRR vectors"
for key in key_order:
relations = filter(
lambda x: x[0] in self.relation_symbols,
self.corpus_dict[key])
if len(relations) == 0:
self.semantic_pointers[key] = HRR(self.dimension)
continue
semantic_pointer = HRR(data=np.zeros(self.dimension))
for n in range(self.sp_noise):
semantic_pointer += HRR(self.dimension)
for relation in relations:
id_vector = self.id_vectors[relation[1]]
relation_type_vector = self.relation_type_vectors[relation[0]]
pair = id_vector * relation_type_vector
semantic_pointer += pair
if self.normalize:
semantic_pointer.normalize()
self.semantic_pointers[key] = semantic_pointer
# convert all vectors from hrrs to numpy ndarrays
for k in key_order:
h = self.semantic_pointers[k]
self.semantic_pointers[k] = h.v
if id_vecs:
for k in key_order:
h = self.id_vectors[k]
self.id_vectors[k] = h.v
for k in self.relation_type_vectors:
h = self.relation_type_vectors[k]
self.relation_type_vectors[k] = h.v
def form_knowledge_base(self, id_vecs=True, unitary=False):
# Check existence of corpus
if self.corpus_dict is None:
raise Exception("Attempted to form the knowledge "
"base without a corpus.")
print "Number of items in knowledge base:", len(self.corpus_dict)
if not id_vecs:
print "Processing Corpus"
self.processCorpus()
print "Generating relation type vectors"
print "Using relation types: ", self.relation_symbols
self.relation_type_vectors = {
symbol: HRR(self.dimension)
for symbol in self.relation_symbols
}
if unitary:
for k, h in self.relation_type_vectors.iteritems():
h.make_unitary()
if id_vecs:
key_order = self.corpus_dict.keys()
else:
# Order words by the dependencies of their definitions
# Only have to do this if we're not using ID-vectors
key_order = []
resolved = set(self.relation_symbols)
dependencies = {}
for key in self.corpus_dict.keys():
dependencies[key] = set([
tag[1] for tag in self.corpus_dict[key]
if tag[0] in self.relation_symbols
])
while len(key_order) < (len(self.corpus_dict) +
len(self.relation_symbols)):
resolvable = set()
for key in dependencies:
if dependencies[key].issubset(resolved):
resolvable.add(key)
# add the resolvable keys to the order list and resolved set
key_order.extend(resolvable)
resolved = resolved.union(resolvable)
# remove resolved tags from the dependency dictionary
for r in resolvable:
del dependencies[r]
# if no items are resolvable, we're stuck
if len(resolvable) == 0:
break
del resolved
del resolvable
if len(key_order) < len(self.corpus_dict):
raise Exception("Dependency resolution failed.")
self.semantic_pointers = collections.OrderedDict()
print "Generating ID-vectors"
if id_vecs:
self.id_vectors = collections.OrderedDict()
for key in key_order:
self.id_vectors[key] = HRR(self.dimension)
else:
self.id_vectors = self.semantic_pointers
print "Generating HRR vectors"
for key in key_order:
relations = filter(lambda x: x[0] in self.relation_symbols,
self.corpus_dict[key])
if len(relations) == 0:
self.semantic_pointers[key] = HRR(self.dimension)
continue
semantic_pointer = HRR(data=np.zeros(self.dimension))
for n in range(self.sp_noise):
semantic_pointer += HRR(self.dimension)
for relation in relations:
id_vector = self.id_vectors[relation[1]]
relation_type_vector = self.relation_type_vectors[relation[0]]
pair = id_vector * relation_type_vector
semantic_pointer += pair
if self.normalize:
semantic_pointer.normalize()
self.semantic_pointers[key] = semantic_pointer
# convert all vectors from hrrs to numpy ndarrays
for k in key_order:
h = self.semantic_pointers[k]
self.semantic_pointers[k] = h.v
if id_vecs:
for k in key_order:
h = self.id_vectors[k]
self.id_vectors[k] = h.v
for k in self.relation_type_vectors:
h = self.relation_type_vectors[k]
self.relation_type_vectors[k] = h.v
def findAllParents(self, start_key, target_key=None, rtype=[],
use_HRR=False, print_output=False):
if print_output:
print >> self.output_file, \
"In find all parents, useHRR=", use_HRR
print >> self.output_file, "Start:", start_key
if target_key is not None:
print >> self.output_file, "Target:", target_key
use_vecs = use_HRR and self.extractor.return_vec
level = 0
if use_vecs:
layerA = [self.semantic_pointers[start_key]]
if target_key:
target_vector = self.semantic_pointers[target_key]
target_hrr = HRR(data=target_vector)
else:
layerA = [start_key]
layerB = []
parents = set()
while len(layerA) > 0:
word = layerA.pop()
# test whether we've found the target
found = False
if use_vecs:
word_hrr = HRR(data=word)
found = target_hrr.compare(word_hrr) > self.decision_threshold
else:
found = word == target_key
if found:
if print_output:
print >> self.output_file, target_key, \
"found at level ", level
return level
if use_vecs:
key = self.get_key_from_vector(word, self.semantic_pointers)
else:
key = word
if key:
if key in parents:
continue
if level > 0:
parents.add(key)
if print_output:
print >> self.output_file, key, \
"found at level ", level
links = []
if not use_HRR:
links = [r[1] for r in self.corpus_dict[word]
if r[0] in rtype]
else:
for symbol in rtype:
answers = [r[1] for r in self.corpus_dict[key]
if r[0] == symbol]
relation_vec = self.relation_type_vectors[symbol]
if len(answers) == 0:
target = None
else:
target = answers[0]
relations = filter(
lambda x: x[0] in self.relation_type_vectors,
self.corpus_dict[key])
num_relations = len(relations)
if use_vecs:
result = self.test_link(
relation_vec, word, key, target,
self.output_file,
return_vec=True, depth=level,
num_relations=num_relations,
answers=answers)
links.append(result)
else:
results = self.test_link(
relation_vec, None, key, target,
self.output_file,
return_vec=False, depth=level,
num_relations=num_relations, answers=answers)
if answers:
results = results[0]
links.extend(results)
if len(links) > 0:
layerB.extend(links)
if len(layerA) == 0:
level = level + 1
layerA = layerB
layerB = []
if target_key is None:
return list(parents)
else:
return -1
def run(self):
self.dimension = len(self.id_vectors.values()[0])
self.role_hrrs = self.create_role_hrrs()
self.pos_map = self.create_pos_map()
score = defaultdict(float)
for i in range(self.num_trials):
title = "New Sentence Test"
if self.deep:
title += "- Deep"
tools.print_header(self.output_file, title)
sentence = self.generate_sentence()
if self.deep:
embed = self.rng.sample(sentence.keys(), 1)[0]
embedded_sentence = self.generate_sentence()
del sentence[embed]
for role in embedded_sentence.keys():
sentence[embed + role] = embedded_sentence[role]
tag_vectors = {}
sentence_hrr = HRR(data=np.zeros(self.dimension))
# Pick role-fillers and create HRR representing the sentence
# Also store the hrr to use as the query to extract each synset
# included in the sentence.
for role in sentence:
tag_hrr = [self.role_hrrs[x] for x in role]
tag_hrr = reduce(lambda x, y: x * y, tag_hrr)
synset = sentence[role]
sentence_hrr += tag_hrr * HRR(data=self.id_vectors[synset])
tag_vectors[role] = tag_hrr.v
sentence_hrr.normalize()
sentence_vector = sentence_hrr.v
print >> self.output_file, "Roles in sentence:"
print >> self.output_file, sentence
# ask about parts of the sentence
sentence_score = defaultdict(float)
sentence_length = defaultdict(float)
for role in sentence.keys():
answer = sentence[role]
self.current_start_key = None
self.current_target_keys = [answer]
self.current_num_relations = len(sentence)
print >> self.output_file, "\nTesting ", role
result, correct, valid, exact = self.test_link(
tag_vectors[role],
sentence_vector,
None,
answer,
output_file=self.output_file,
return_vec=False,
num_relations=len(sentence),
answers=[answer])
depth = len(role)
if correct:
sentence_score[depth] += 1
print >> self.output_file, "Correct."
else:
print >> self.output_file, "Incorrect."
sentence_length[depth] += 1
if self.short:
break
for d in sentence_length:
sentence_percent = sentence_score[d] / sentence_length[d]
print >> self.output_file, \
"Percent correct for current sentence at depth %d: %f" \
% (d, sentence_percent)
score[d] = score[d] + sentence_percent
for d in score:
print "Sentence test score at depth %d: %f out of %d" \
% (d, score[d], self.num_trials)
percent = score[d] / self.num_trials
title = "Sentence Test Summary - Depth = %d" % d
tools.print_header(self.output_file, title)
print >> self.output_file, "Correct: ", score[d]
print >> self.output_file, "Total: ", self.num_trials
print >> self.output_file, "Percent: ", percent
tools.print_footer(self.output_file, title)
self.add_data("sentence_score_%d" % d, percent)
def run(self):
self.dimension = len(self.id_vectors.values()[0])
self.role_hrrs = self.create_role_hrrs()
self.pos_map = self.create_pos_map()
score = defaultdict(float)
for i in range(self.num_trials):
title = "New Sentence Test"
if self.deep:
title += "- Deep"
tools.print_header(self.output_file, title)
sentence = self.generate_sentence()
if self.deep:
embed = self.rng.sample(sentence.keys(), 1)[0]
embedded_sentence = self.generate_sentence()
del sentence[embed]
for role in embedded_sentence.keys():
sentence[embed + role] = embedded_sentence[role]
tag_vectors = {}
sentence_hrr = HRR(data=np.zeros(self.dimension))
# Pick role-fillers and create HRR representing the sentence
# Also store the hrr to use as the query to extract each synset
# included in the sentence.
for role in sentence:
tag_hrr = [self.role_hrrs[x] for x in role]
tag_hrr = reduce(lambda x, y: x * y, tag_hrr)
synset = sentence[role]
sentence_hrr += tag_hrr * HRR(data=self.id_vectors[synset])
tag_vectors[role] = tag_hrr.v
sentence_hrr.normalize()
sentence_vector = sentence_hrr.v
print >> self.output_file, "Roles in sentence:"
print >> self.output_file, sentence
# ask about parts of the sentence
sentence_score = defaultdict(float)
sentence_length = defaultdict(float)
for role in sentence.keys():
answer = sentence[role]
self.current_start_key = None
self.current_target_keys = [answer]
self.current_num_relations = len(sentence)
print >> self.output_file, "\nTesting ", role
result, correct, valid, exact = self.test_link(
tag_vectors[role], sentence_vector, None, answer,
output_file=self.output_file, return_vec=False,
num_relations=len(sentence), answers=[answer])
depth = len(role)
if correct:
sentence_score[depth] += 1
print >> self.output_file, "Correct."
else:
print >> self.output_file, "Incorrect."
sentence_length[depth] += 1
if self.short:
break
for d in sentence_length:
sentence_percent = sentence_score[d] / sentence_length[d]
print >> self.output_file, \
"Percent correct for current sentence at depth %d: %f" \
% (d, sentence_percent)
score[d] = score[d] + sentence_percent
for d in score:
print "Sentence test score at depth %d: %f out of %d" \
% (d, score[d], self.num_trials)
percent = score[d] / self.num_trials
title = "Sentence Test Summary - Depth = %d" % d
tools.print_header(self.output_file, title)
print >> self.output_file, "Correct: ", score[d]
print >> self.output_file, "Total: ", self.num_trials
print >> self.output_file, "Percent: ", percent
tools.print_footer(self.output_file, title)
self.add_data("sentence_score_%d" % d, percent)
def find_matches(self, vector, vector_dict, exempt=[]):
hrr_vec = HRR(data=vector)
for key in vector_dict.keys():
if key not in exempt:
yield (key, hrr_vec.compare(HRR(data=vector_dict[key])))
def plot_simulation(self, target_keys):
then = datetime.datetime.now()
correct_key = None
if target_keys:
correct_key = target_keys[0]
sim = self.simulator
t = sim.trange()
max_val = 5.0 / np.sqrt(self.dimension)
gs = gridspec.GridSpec(7, 2)
fig = plt.figure(figsize=(10, 10))
ax = plt.subplot(gs[0, 0])
plt.plot(t, self.data[self.D_probe], label='D')
title = 'Input to associative memory'
ax.text(.01,
1.20,
title,
horizontalalignment='left',
transform=ax.transAxes)
plt.ylim((-max_val, max_val))
ax = plt.subplot(gs[0, 1])
plt.plot(t, self.data[self.output_probe], label='Output')
title = 'Output of associative memory'
ax.text(.01,
1.20,
title,
horizontalalignment='left',
transform=ax.transAxes)
plt.ylim((-max_val, max_val))
ax = plt.subplot(gs[1:3, :])
if len(self.index_vectors) < 1000:
for key, v in self.index_vectors.iteritems():
input_sims = np.dot(self.data[self.D_probe], v)
label = str(key[1])
if key == correct_key:
plt.plot(t, input_sims, '--', label=label + '*')
else:
plt.plot(t, input_sims, label=label)
title = (
'Dot product between id vectors and input to assoc memory.\n'
'Target %s is dashed line.' % str(correct_key))
ax.text(.01,
0.80,
title,
horizontalalignment='left',
transform=ax.transAxes)
# plt.legend(bbox_to_anchor=(-0.03, 0.5), loc='center right')
if self.ideal_dot:
ax.text(.01,
0.10,
"Ideal dot: " + str(self.ideal_dot),
horizontalalignment='left',
transform=ax.transAxes)
if self.second_dot:
ax.text(.99,
0.10,
"Second dot: " + str(self.second_dot),
horizontalalignment='right',
transform=ax.transAxes)
plt.ylim((-1.0, 1.5))
plt.axhline(1.0, ls=':', c='k')
ax = plt.subplot(gs[3:5, :])
for key, p in self.assoc_probes.iteritems():
if key == correct_key:
plt.plot(t, self.data[p], '--')
else:
plt.plot(t, self.data[p])
title = ('Decoded values of association populations.\n' +
'Target %s is dashed line.' % str(correct_key))
ax.text(.01,
0.80,
title,
horizontalalignment='left',
transform=ax.transAxes)
plt.ylim((-0.2, 1.5))
plt.axhline(y=1.0, ls=':', c='k')
ax = plt.subplot(gs[5:7, :])
before_ls = '--'
after_ls = '-'
before_norms = [np.linalg.norm(v) for v in self.data[self.D_probe]]
after_norms = [np.linalg.norm(v) for v in self.data[self.output_probe]]
plt.plot(t, before_norms, before_ls, c='g', label='Norm - Before')
plt.plot(t, after_norms, after_ls, c='g', label='Norm - After')
if correct_key is not None:
correct_index_hrr = HRR(data=self.index_vectors[correct_key])
correct_stored_hrr = HRR(data=self.stored_vectors[correct_key])
before_sims = [
correct_index_hrr.compare(HRR(data=i))
for i in self.data[self.D_probe]
]
after_sims = [
correct_stored_hrr.compare(HRR(data=o))
for o in self.data[self.output_probe]
]
plt.plot(t,
before_sims,
before_ls,
c='b',
label='Cosine Sim - Before')
plt.plot(t,
after_sims,
after_ls,
c='b',
label='Cosine Sim - After')
title = 'Before and After Associative Memory'
ax.text(.01,
0.90,
title,
horizontalalignment='left',
transform=ax.transAxes)
plt.ylim((-1.0, 1.5))
plt.legend(loc=4, prop={'size': 6})
plt.axhline(y=1.0, ls=':', c='k')
ax.set_xlabel('Time (s)')
date_time_string = str(datetime.datetime.now()).split('.')[0]
date_time_string = reduce(lambda y, z: string.replace(y, z, "_"),
[date_time_string, ":", ".", " ", "-"])
plot_name = 'neural_extraction_' + date_time_string + ".png"
plot_path = os.path.join(self.output_dir, plot_name)
plt.savefig(plot_path)
symlink_name = os.path.join(self.output_dir,
'latest_neural_extraction')
make_sym_link(plot_name, symlink_name)
now = datetime.datetime.now()
self.write_to_runtime_file(now - then, "plot")
plt.close(fig)
def find_matches(self, vector, vector_dict, exempt=[]):
hrr_vec = HRR(data=vector)
for key in vector_dict.keys():
if key not in exempt:
yield (key, hrr_vec.compare(HRR(data=vector_dict[key])))
