def test_from_entries_and_from_matrix():
# Note: for convenience from_matrix() is tested here implicitly, rather
# than in a separate test.
# Reject outright a space with no entries and a space with insufficient
# entries
assert AssocSpace.from_entries([], k=1) is None
assert AssocSpace.from_entries([(1, 'apple', 'red')], k=1) is None
# Build with mostly-default parameters and check some simple properties
assoc_default = AssocSpace.from_entries(ENTRIES, k=4)
eq_(assoc_default.k, 4)
eq_(assoc_default.sigma[0], 1.0)
assert assoc_default.assoc_between_two_terms('apple', 'red') > 0.5
assert assoc_default.assoc_between_two_terms('red', 'red') > 0.999
assert assoc_default.assoc_between_two_terms('lemon', 'red') < 0.2
# Build with strip_a0=False; in this case we have negative eigenvalues,
# so we lose an eigenvalue from the middle to make room for a0
assoc_no_strip = AssocSpace.from_entries(ENTRIES, k=4, strip_a0=False)
eq_(assoc_no_strip.k, 4)
assert np.allclose(assoc_no_strip.sigma[-1] / assoc_no_strip.sigma[1],
assoc_default.sigma[-1])
assert (np.allclose(assoc_no_strip.u[:, 1], assoc_default.u[:, 0])
or np.allclose(assoc_no_strip.u[:, 1], -assoc_default.u[:, 0]))
# Build with normalize_gm=False
assoc_no_norm = AssocSpace.from_entries(ENTRIES, k=4, normalize_gm=False)
eq_(assoc_no_norm.k, 4)
def merge_vector_spaces(subspace_dir, mergers):
merged = None
for sourceA, sourceB, target in mergers:
print('Merging: %s + %s -> %s' % (sourceA, sourceB, target))
spaceA = AssocSpace.load_dir(os.path.join(subspace_dir, sourceA))
spaceB = AssocSpace.load_dir(os.path.join(subspace_dir, sourceB))
# On the first step, we want to keep all the axes from merging subparts.
# Through most of the merging, we want to maintain that number of axes.
# At the end, we want to go back to the original number of axes.
# For example, when we are merging 300-dimensional spaces, the
# intermediate merge results will have 600 dimensions, and the final
# result will have 300 dimensions again.
# We don't refer to the number of axes in spaceB in this code, because
# we're assuming all the sub-parts have equal numbers of axes.
if target.startswith('part'):
k = spaceA.k * 2
elif target == 'merged_complete':
k = spaceA.k // 2
else:
k = spaceA.k
merged = spaceA.merged_with(spaceB, k=k)
del spaceA
del spaceB
merged.save_dir(os.path.join(subspace_dir, target))
magnitudes = (merged.u ** 2).sum(1)
good_indices = np.flatnonzero(magnitudes >= 1e-5)
filtered = merged[good_indices]
filtered.save_dir(os.path.join(subspace_dir, 'merged_filtered'))
return filtered
def test_from_entries_and_from_matrix():
# Note: for convenience from_matrix() is tested here implicitly, rather
# than in a separate test.
# Reject outright a space with no entries and a space with insufficient
# entries
assert AssocSpace.from_entries([], k=1) is None
assert AssocSpace.from_entries([(1, 'apple', 'red')], k=1) is None
# Build with mostly-default parameters and check some simple properties
assoc_default = AssocSpace.from_entries(ENTRIES, k=4)
eq_(assoc_default.k, 4)
eq_(assoc_default.sigma[0], 1.0)
assert assoc_default.assoc_between_two_terms('apple', 'red') > 0.5
assert assoc_default.assoc_between_two_terms('red', 'red') > 0.999
assert assoc_default.assoc_between_two_terms('lemon', 'red') < 0.2
# Build with strip_a0=False; in this case we have negative eigenvalues,
# so we lose an eigenvalue from the middle to make room for a0
assoc_no_strip = AssocSpace.from_entries(ENTRIES, k=4, strip_a0=False)
eq_(assoc_no_strip.k, 4)
assert np.allclose(assoc_no_strip.sigma[-1] / assoc_no_strip.sigma[1],
assoc_default.sigma[-1])
assert (np.allclose(assoc_no_strip.u[:, 1], assoc_default.u[:, 0]) or
np.allclose(assoc_no_strip.u[:, 1], -assoc_default.u[:, 0]))
# Build with normalize_gm=False
assoc_no_norm = AssocSpace.from_entries(ENTRIES, k=4, normalize_gm=False)
eq_(assoc_no_norm.k, 4)
def build_assoc_space(input_file, output_dir):
print('loading')
counts = defaultdict(int)
triples = []
for line in codecs.open(input_file, encoding='utf-8'):
left, right, value = line.strip().split('\t')
if not concept_is_bad(left) and not concept_is_bad(right):
value = float(value)
triples.append((value, left, right))
counts[left] += 1
counts[right] += 1
print('filtering entries')
sparse = SparseEntryStorage()
for (value, left, right) in triples:
if concept_is_frequent_enough(left,
counts) and concept_is_frequent_enough(
right, counts) and left != right:
sparse.add_entry((value, left, right))
del triples
# Add links from a concept to itself, and negative links to its opposite if it's there
for concept in counts:
if concept_is_frequent_enough(concept, counts):
sparse.add_entry((1., concept, concept))
negation = negate_concept(concept)
if concept_is_frequent_enough(negation, counts):
sparse.add_entry((-1., concept, negation))
print('making assoc space')
space = AssocSpace.from_sparse_storage(sparse, 150, offset_weight=4e-5)
print('saving')
space.save_dir(output_dir)
def test_vectorizing_and_similar_terms():
# Simple test for vectorizing weighted terms
assoc = AssocSpace.from_entries(ENTRIES, k=3)
weighted_terms = [('apple', 5), ('banana', 22), ('not a term', 17)]
apple = assoc.row_named('apple')
banana = assoc.row_named('banana')
vector = assoc.vector_from_terms(weighted_terms)
# The similarity of 'apple' to itself is approximately 1
assert abs(assoc.assoc_between_two_terms('apple', 'apple') - 1.0) < 1e-3
# 'apple' and 'banana' are at least 10% less similar to each other than
# to themselves
assert assoc.assoc_between_two_terms('apple', 'banana') < 0.9
# The vector is some linear combination of apple and banana. Test this
# by subtracting out apple and banana components, so that there is nothing
# left.
norm_apple = normalize(apple)
banana_perp_apple = normalize(banana - norm_apple * norm_apple.dot(banana))
residual = vector - norm_apple * norm_apple.dot(vector)
residual -= banana_perp_apple * banana_perp_apple.dot(residual)
assert norm(residual) < 1e-3
# Simple test for finding similar terms
labels, scores = zip(*assoc.terms_similar_to_vector(vector))
eq_(list(scores), sorted(scores, reverse=True))
most_similar = assoc.most_similar_to_vector(vector)
eq_(most_similar[0], labels[0])
eq_(most_similar[1], scores[0])
assert labels.index('banana') < labels.index('apple')
assert labels.index('apple') < labels.index('green')
assert labels.index('apple') < labels.index('celery')
def test_vectorizing_and_similar_terms():
# Simple test for vectorizing weighted terms
assoc = AssocSpace.from_entries(ENTRIES, k=3)
weighted_terms = [('apple', 5), ('banana', 22), ('not a term', 17)]
apple = assoc.row_named('apple')
banana = assoc.row_named('banana')
vector = assoc.vector_from_terms(weighted_terms)
# The similarity of 'apple' to itself is approximately 1
assert abs(assoc.assoc_between_two_terms('apple', 'apple') - 1.0) < 1e-3
# 'apple' and 'banana' are at least 10% less similar to each other than
# to themselves
assert assoc.assoc_between_two_terms('apple', 'banana') < 0.9
# The vector is some linear combination of apple and banana. Test this
# by subtracting out apple and banana components, so that there is nothing
# left.
norm_apple = normalize(apple)
banana_perp_apple = normalize(banana - norm_apple * norm_apple.dot(banana))
residual = vector - norm_apple * norm_apple.dot(vector)
residual -= banana_perp_apple * banana_perp_apple.dot(residual)
assert norm(residual) < 1e-3
# Simple test for finding similar terms
labels, scores = zip(*assoc.terms_similar_to_vector(vector))
eq_(list(scores), sorted(scores, reverse=True))
most_similar = assoc.most_similar_to_vector(vector)
eq_(most_similar[0], labels[0])
eq_(most_similar[1], scores[0])
assert labels.index('banana') < labels.index('apple')
assert labels.index('apple') < labels.index('green')
assert labels.index('apple') < labels.index('celery')
def build_assoc_space(input_file, output_dir):
print('loading')
counts = defaultdict(int)
triples = []
for line in codecs.open(input_file, encoding='utf-8'):
left, right, value = line.strip().split('\t')[:3]
if not concept_is_bad(left) and not concept_is_bad(right):
value = float(value)
triples.append((value, left, right))
counts[left] += 1
counts[right] += 1
print('filtering entries')
sparse = SparseEntryStorage()
for (value, left, right) in triples:
if concept_is_frequent_enough(left, counts) and concept_is_frequent_enough(right, counts) and left != right:
sparse.add_entry((value, left, right))
del triples
# Add links from a concept to itself, and negative links to its opposite if it's there
for concept in counts:
if concept_is_frequent_enough(concept, counts):
sparse.add_entry((1., concept, concept))
negation = negate_concept(concept)
if concept_is_frequent_enough(negation, counts):
sparse.add_entry((-1., concept, negation))
print('making assoc space')
space = AssocSpace.from_sparse_storage(sparse, k=300, offset_weight=1e-4)
print('saving')
space.save_dir(output_dir)
def optimizeAllAndInferConceptsModelTwo(assocDir):
## load assocSpace
assocSpace = AssocSpace.load_dir(assocDir);
## targets and image-indices dictionary
targetsToImageIndicesAndWeights={}; # target-word ->[(index,weight_i)...]
targetsToCentralities={}; #target-word -> centrality-score
loadTargetWordsFromAllImages(targetsToCentralities,targetsToImageIndicesAndWeights);
# Model
m = Model("psl2")
variables= set();
targets = {}
loadDecisionVariablesForTargets(m,targets,variables,targetsToImageIndicesAndWeights);
## TODO: populate the rules
objective = LinExpr();
objective = createObjective(m,targets,variables,objective,assocSpace,targetsToCentralities,targetsToImageIndicesAndWeights);
m.update();
m.setObjective(objective);
# The objective is to minimize the costs
m.modelSense = GRB.MINIMIZE
# Update model to integrate new variables
m.update()
m.optimize();
m.write('out2.lp');
m.write('out2.sol');
outputFile = open(sys.argv[1]+ sys.argv[2]+"_inferred.txt","w");
printSolution(m,targets,outputFile);
def test_truncation():
# Simple test of truncation
assoc = AssocSpace.from_entries(ENTRIES, k=3)
truncated = assoc.truncated_to(2)
assert np.allclose(truncated.u, assoc.u[:, :2])
assert np.allclose(truncated.sigma, assoc.sigma[:2])
eq_(truncated.labels, assoc.labels)
assert 0.999 < norm(truncated.assoc[0]) < 1.0
def test_truncation():
# Simple test of truncation
assoc = AssocSpace.from_entries(ENTRIES, k=3)
truncated = assoc.truncated_to(2)
assert np.allclose(truncated.u, assoc.u[:, :2])
assert np.allclose(truncated.sigma, assoc.sigma[:2])
eq_(truncated.labels, assoc.labels)
assert 0.999 < norm(truncated.assoc[0]) < 1.0
def test_strip_a0():
"""When stripping a0, AssocSpace uses axes [1,k] instead of [0,k-1]."""
assoc = AssocSpace.from_entries(entries, 3, strip_a0=False)
assoc_stripped_mat = AssocSpace.from_entries(entries, 3, strip_a0=True)
# Check for the same number of k
eq_(assoc.u.shape[1], 3)
assert np.allclose(np.abs(assoc.u[:,1]), np.abs(assoc_stripped_mat.u[:,0]))
# check that the ratio between sigma values is preserved
assert np.allclose(assoc.sigma[1] / assoc.sigma[2],
assoc_stripped_mat.sigma[0] / assoc_stripped_mat.sigma[1])
assoc_stripped_dropa0 = AssocSpace.from_entries(entries, 3).with_first_axis_dropped()
assert np.allclose(np.abs(assoc.u[:,1]),
np.abs(assoc_stripped_dropa0.u[:,0]))
assert np.allclose(assoc.sigma[1] / assoc.sigma[2],
assoc_stripped_dropa0.sigma[0] / assoc_stripped_dropa0.sigma[1])
def load_assoc():
"""
Load the association matrix. Requires the open source Python package
'assoc_space'.
"""
global commonsense_assoc
if commonsense_assoc: return commonsense_assoc
dirname = ASSOC_DIR
commonsense_assoc = AssocSpace.load_dir(ASSOC_DIR)
return commonsense_assoc
def load_assoc():
"""
Load the association matrix. Requires the open source Python package
'assoc_space'.
"""
global commonsense_assoc
if commonsense_assoc: return commonsense_assoc
dirname = ASSOC_DIR
commonsense_assoc = AssocSpace.load_dir(ASSOC_DIR)
return commonsense_assoc
def test_merging():
# The actual math of merging is tested separately in test_eigenmath; here
# we just spot-verify that AssocSpace is using it reasonably
# Generate test assoc spaces and merge them
assoc1 = AssocSpace.from_entries(ENTRIES, k=4)
assoc2 = AssocSpace.from_entries(MORE_ENTRIES, k=4)
merged = assoc1.merged_with(assoc2)
eq_(merged.k, 8)
# Check some simple things
merged = assoc1.merged_with(assoc2, k=4)
eq_(merged.k, 4)
eq_(' '.join(merged.labels),
'apple red green celery orange banana yellow lemon blue tasty ferret')
assert merged.assoc_between_two_terms('ferret', 'yellow') > 0.5
assert (assoc2.assoc_between_two_terms(
'apple', 'red') < merged.assoc_between_two_terms('apple', 'red') <
assoc1.assoc_between_two_terms('apple', 'red'))
def test_merging():
# The actual math of merging is tested separately in test_eigenmath; here
# we just spot-verify that AssocSpace is using it reasonably
# Generate test assoc spaces and merge them
assoc1 = AssocSpace.from_entries(ENTRIES, k=4)
assoc2 = AssocSpace.from_entries(MORE_ENTRIES, k=4)
merged = assoc1.merged_with(assoc2)
eq_(merged.k, 8)
# Check some simple things
merged = assoc1.merged_with(assoc2, k=4)
eq_(merged.k, 4)
eq_(' '.join(merged.labels),
'apple red green celery orange banana yellow lemon blue tasty ferret')
assert merged.assoc_between_two_terms('ferret', 'yellow') > 0.5
assert (assoc2.assoc_between_two_terms('apple', 'red') <
merged.assoc_between_two_terms('apple', 'red') <
assoc1.assoc_between_two_terms('apple', 'red'))
def load(self):
if self.assoc is not None:
return
try:
from assoc_space import AssocSpace
self.assoc = AssocSpace.load_dir(self.path)
except ImportError:
raise MissingAssocSpace("The assoc_space package is not installed.")
except ZeroDivisionError:
raise MissingAssocSpace("The space of term associations could not "
"be loaded.")
def test_assoc_constructor():
# Make a nice, normal AssocSpace
u = np.asarray([[0, 1, 0.6], [1, 0, 0.8]])
sigma = np.asarray([0.5, 0.3, 0.2])
labels = LabelSet(['A', 'B'])
assoc = AssocSpace(u, sigma, labels)
eq_(assoc.k, 3)
assert 'assoc' not in assoc.__dict__
# Test some error conditions
with assert_raises(ValueError):
AssocSpace(u, np.asarray([0.0, -0.2, -0.4]), labels)
with assert_raises(ValueError):
AssocSpace(u, np.asarray([0.6, 0.4]), labels)
with assert_raises(ValueError):
AssocSpace(u, np.asarray([0.6, 0.7, 0.2]), labels)
# Test assoc hinting
assoc_matrix = assoc.assoc.copy()
assoc_hinted = AssocSpace(u, sigma, labels, assoc=assoc_matrix)
assert np.allclose(assoc_hinted.row_named('A'), assoc.row_named('A'))
def load(self):
if self.assoc is not None:
return
try:
from assoc_space import AssocSpace
self.assoc = AssocSpace.load_dir(self.path)
except ImportError:
raise MissingAssocSpace(
"The assoc_space package is not installed.")
except ZeroDivisionError:
raise MissingAssocSpace("The space of term associations could not "
"be loaded.")
def test_assoc_constructor():
# Make a nice, normal AssocSpace
u = np.asarray([[0, 1, 0.6], [1, 0, 0.8]])
sigma = np.asarray([0.5, 0.3, 0.2])
labels = LabelSet(['A', 'B'])
assoc = AssocSpace(u, sigma, labels)
eq_(assoc.k, 3)
assert 'assoc' not in assoc.__dict__
# Test some error conditions
with assert_raises(ValueError):
AssocSpace(u, np.asarray([0.0, -0.2, -0.4]), labels)
with assert_raises(ValueError):
AssocSpace(u, np.asarray([0.6, 0.4]), labels)
with assert_raises(ValueError):
AssocSpace(u, np.asarray([0.6, 0.7, 0.2]), labels)
# Test assoc hinting
assoc_matrix = assoc.assoc.copy()
assoc_hinted = AssocSpace(u, sigma, labels, assoc=assoc_matrix)
assert np.allclose(assoc_hinted.row_named('A'), assoc.row_named('A'))
def test_filter():
# Build and filter an assoc space
assoc = AssocSpace.from_entries(ENTRIES, k=5)
filtered = assoc.filter(_filter)
# Check simple properties of the filtered space
eq_(filtered.k, 5)
eq_(' '.join(filtered.labels), 'red green celery banana lemon')
# Check that redecomposition happened
assert np.allclose(norm(filtered.u[:, 1]), 1.0)
# Redecomposition can be kind of weird, but this result is intuitive
assert (assoc.assoc_between_two_terms('red', 'banana') <
filtered.assoc_between_two_terms('red', 'banana') <
assoc.assoc_between_two_terms('yellow', 'banana'))
def test_filter():
# Build and filter an assoc space
assoc = AssocSpace.from_entries(ENTRIES, k=5)
filtered = assoc.filter(_filter)
# Check simple properties of the filtered space
eq_(filtered.k, 5)
eq_(' '.join(filtered.labels), 'red green celery banana lemon')
# Check that redecomposition happened
assert np.allclose(norm(filtered.u[:, 1]), 1.0)
# Redecomposition can be kind of weird, but this result is intuitive
assert (assoc.assoc_between_two_terms(
'red', 'banana') < filtered.assoc_between_two_terms('red', 'banana') <
assoc.assoc_between_two_terms('yellow', 'banana'))
def run():
ENTRIES = [
(4, '/c/en/apple', '/c/en/red'),
(1, '/c/en/apple', '/c/en/green'),
(3, '/c/en/apple', '/c/en/orange'),
(3, '/c/en/banana', '/c/en/orange'),
(1, '/c/en/banana', '/c/en/yellow'),
(0.5, '/c/en/lemon', '/c/en/yellow'),
(1.5, '/c/en/orange', '/c/en/lemon'),
(0.1, '/c/en/apple', '/c/en/lemon'),
(0.2, '/c/en/banana', '/c/en/lemon'),
(0.5, '/c/en/ideas', '/c/en/colorless'),
(0.5, '/c/en/ideas', '/c/en/green'),
(1, '/c/en/example', '/c/en/green'),
]
space = AssocSpace.from_entries(ENTRIES, k=4)
space.save_dir('../conceptnet5/support_data/testdata/input/assoc_space')
from assoc_space import AssocSpace
import sys
import threading
import math
def computeNormalizedValue(value, maxV, minV, addOne=False):
if addOne:
return (value - minV + 1) / (maxV - minV + 1)
return (value - minV) / (maxV - minV)
if len(sys.argv) < 4:
print "python conceptnetAssocSpace.py <seedsfile> <targetfile> <AssocSpaceDirectory>"
sys.exit()
assocSpace = AssocSpace.load_dir(sys.argv[3])
words = []
minSimilarity = -0.358846
maxSimilarity = 0.999747
minCentrality = -0.00188222
maxCentrality = 0.00324597
with open(sys.argv[1], "r") as f:
i = 0
for line in f:
if line.startswith("##"):
continue
words = line.split("\t")
word1 = "/c/en/" + words[0].strip()
with open(sys.argv[2], "r") as f2:
for line in f2:
if line.startswith("##"):
def test_pickle_round_trip():
"""An AssocSpace survives a round-trip to pickle format and back."""
assoc = AssocSpace.from_entries(entries, 3)
pickled = pickle.dumps(assoc)
assoc2 = pickle.loads(pickled)
eq_(assoc, assoc2)
def test_dir_round_trip():
assoc = AssocSpace.from_entries(ENTRIES, k=3)
assoc.save_dir('/tmp/assoc_test')
assoc2 = AssocSpace.load_dir('/tmp/assoc_test')
eq_(assoc, assoc2)
def test_dir_round_trip():
assoc = AssocSpace.from_entries(entries, 3)
assoc.save_dir('/tmp/assoc_test')
assoc2 = AssocSpace.load_dir('/tmp/assoc_test')
eq_(assoc, assoc2)
def test_pickle_round_trip():
"""An AssocSpace survives a round-trip to pickle format and back."""
assoc = AssocSpace.from_entries(ENTRIES, k=3)
pickled = pickle.dumps(assoc)
assoc2 = pickle.loads(pickled)
eq_(assoc, assoc2)
def test_association_calculations():
assoc = AssocSpace.from_entries(entries, 3)
assert abs(assoc.assoc_between_two_terms('apple', 'apple') - 1.0) < 1e-3
assert assoc.assoc_between_two_terms('apple', 'banana') < 0.9
def main(dir):
assoc = AssocSpace.load_dir(dir)
test(assoc)
def main(dir): assoc = AssocSpace.load_dir(dir) test(assoc)
if not os.path.isfile(sortedIndicesFileName):
sim = assocSpace.assoc.dot(assocSpace.row_named("/c/en/" + word))
indices = np.argsort(sim)[::-1]
np.savez_compressed(sortedIndicesFileName, indices[:1000])
sim_first1k = np.array([sim[index] for index in indices[:1000]])
np.savez_compressed(simFileName, sim_first1k)
sim = np.load(simFileName)
indices = np.load(sortedIndicesFileName)
data = []
for index in indices:
if len(data) == limit:
break
if filterEnglishWords(names[index]):
data.append((names[index], sim[index]))
return data
minSimilarity = -1
maxSimilarity = 1
minCentrality = -0.00188222
maxCentrality = 0.00324597
assocDir = "../conceptnet5/data/assoc/assoc-space-5.4"
assocSpace = AssocSpace.load_dir(assocDir)
names = assocSpace.labels
word2vec_model = models.word2vec.Word2Vec.load_word2vec_format(
'../../../DATASETS/GoogleNews-vectors-negative300.bin', binary=True)
word2vec_model.init_sims(replace=True)
TOO_RARE_WORD_CODE = -3
NOT_FOUND_IN_CORPUS_CODE = -2