def test_compile_no_deps(self):
# Defines a label matrix
L = sparse.lil_matrix((5, 3))
# The first LF always says yes
L[0, 0] = 1
L[1, 0] = 1
L[2, 0] = 1
L[3, 0] = 1
L[4, 0] = 1
# The second LF votes differently
L[0, 1] = 1
L[2, 1] = -1
L[4, 1] = 1
# The third LF always abstains
# Tests compilation
gen_model = GenerativeModel(class_prior=True,
lf_prior=False,
lf_propensity=False,
lf_class_propensity=False)
gen_model._process_dependency_graph(L, ())
m, n = L.shape
LF_acc_prior_weights = [1.0 for _ in range(n)]
is_fixed = [False for _ in range(n)]
gen_model.cardinality = 2
cardinalities = 2 * np.ones(5)
weight, variable, factor, ftv, domain_mask, n_edges =\
gen_model._compile(L, 0.5, 0.0, LF_acc_prior_weights, is_fixed,
cardinalities)
#
# Weights
#
# Should now be 3 for LFs + 3 (fixed) for LF priors + 1 class prior
self.assertEqual(len(weight), 7)
self.assertFalse(weight[0]['isFixed'])
self.assertEqual(weight[0]['initialValue'], 0.0)
# The LF priors
for i in range(1, 7, 2):
self.assertTrue(weight[i]['isFixed'])
self.assertEqual(weight[i]['initialValue'], 1.0)
# The LF weights
for i in range(2, 7, 2):
self.assertFalse(weight[i]['isFixed'])
self.assertEqual(weight[i]['initialValue'], 0.0)
#
# Variables
#
self.assertEqual(len(variable), 20)
for i in range(5):
self.assertEqual(variable[i]['isEvidence'], 0)
self.assertTrue(variable[i]['initialValue'] == 0
or variable[i]['initialValue'] == 1)
self.assertEqual(variable[i]["dataType"], 0)
self.assertEqual(variable[i]["cardinality"], 2)
for i in range(5):
for j in range(3):
self.assertEqual(variable[5 + i * 3 + j]['isEvidence'], 1)
# Remap label value; abstain is 0 in L, cardinality (= 2) in NS
if L[i, j] == -1:
l = 0
elif L[i, j] == 0:
l = 2
elif L[i, j] == 1:
l = 1
self.assertEqual(variable[5 + i * 3 + j]['initialValue'], l)
self.assertEqual(variable[5 + i * 3 + j]["dataType"], 0)
self.assertEqual(variable[5 + i * 3 + j]["cardinality"], 3)
#
# Factors
#
# 5 * 3 LF acc factors + 5 * 3 LF prior factors + 5 class prior factors
self.assertEqual(len(factor), 35)
for i in range(5):
self.assertEqual(factor[i]["factorFunction"],
FACTORS["DP_GEN_CLASS_PRIOR"])
self.assertEqual(factor[i]["weightId"], 0)
self.assertEqual(factor[i]["featureValue"], 1)
self.assertEqual(factor[i]["arity"], 1)
self.assertEqual(factor[i]["ftv_offset"], i)
for i in range(5):
for j in range(6):
self.assertEqual(factor[5 + i * 6 + j]["factorFunction"],
FACTORS["DP_GEN_LF_ACCURACY"])
self.assertEqual(factor[5 + i * 6 + j]["weightId"], j + 1)
self.assertEqual(factor[5 + i * 6 + j]["featureValue"], 1)
self.assertEqual(factor[5 + i * 6 + j]["arity"], 2)
self.assertEqual(factor[5 + i * 6 + j]["ftv_offset"],
5 + 2 * (i * 6 + j))
#
# Factor to Var
#
self.assertEqual(len(ftv), 65)
# Class prior factor - var edges
for i in range(5):
self.assertEqual(ftv[i]["vid"], i)
self.assertEqual(ftv[i]["dense_equal_to"], 0)
# LF *and LF prior* factor - var edges
for i in range(5):
for j in range(3):
# Each LF has one weight factor and one prior factor here
for k in range(2):
idx = 4 * (i * 3 + j) + 2 * k
self.assertEqual(ftv[5 + idx]["vid"], i)
self.assertEqual(ftv[6 + idx]["vid"], 5 + i * 3 + j)
self.assertEqual(ftv[5 + idx]["dense_equal_to"], 0)
self.assertEqual(ftv[6 + idx]["dense_equal_to"], 0)
#
# Domain mask
#
self.assertEqual(len(domain_mask), 20)
for i in range(20):
self.assertFalse(domain_mask[i])
# n_edges
self.assertEqual(n_edges, 65)
def test_compile_no_deps(self):
# Defines a label matrix
L = sparse.lil_matrix((5, 3))
# The first LF always says yes
L[0, 0] = 1
L[1, 0] = 1
L[2, 0] = 1
L[3, 0] = 1
L[4, 0] = 1
# The second LF votes differently
L[0, 1] = 1
L[2, 1] = -1
L[4, 1] = 1
# The third LF always abstains
# Tests compilation
gen_model = GenerativeModel(class_prior=True, lf_prior=False,
lf_propensity=False, lf_class_propensity=False)
gen_model._process_dependency_graph(L, ())
m, n = L.shape
LF_acc_prior_weights = [1.0 for _ in range(n)]
is_fixed = [False for _ in range(n)]
gen_model.cardinality = 2
cardinalities = 2 * np.ones(5)
weight, variable, factor, ftv, domain_mask, n_edges =\
gen_model._compile(L, 0.5, 0.0, LF_acc_prior_weights, is_fixed,
cardinalities)
#
# Weights
#
# Should now be 3 for LFs + 3 (fixed) for LF priors + 1 class prior
self.assertEqual(len(weight), 7)
self.assertFalse(weight[0]['isFixed'])
self.assertEqual(weight[0]['initialValue'], 0.0)
# The LF priors
for i in range(1,7,2):
self.assertTrue(weight[i]['isFixed'])
self.assertEqual(weight[i]['initialValue'], 1.0)
# The LF weights
for i in range(2,7,2):
self.assertFalse(weight[i]['isFixed'])
self.assertEqual(weight[i]['initialValue'], 0.0)
#
# Variables
#
self.assertEqual(len(variable), 20)
for i in range(5):
self.assertEqual(variable[i]['isEvidence'], 0)
self.assertTrue(variable[i]['initialValue'] == 0 or variable[i]['initialValue'] == 1)
self.assertEqual(variable[i]["dataType"], 0)
self.assertEqual(variable[i]["cardinality"], 2)
for i in range(5):
for j in range(3):
self.assertEqual(variable[5 + i * 3 + j]['isEvidence'], 1)
# Remap label value; abstain is 0 in L, cardinality (= 2) in NS
if L[i, j] == -1:
l = 0
elif L[i, j] == 0:
l = 2
elif L[i,j] == 1:
l = 1
self.assertEqual(variable[5 + i * 3 + j]['initialValue'], l)
self.assertEqual(variable[5 + i * 3 + j]["dataType"], 0)
self.assertEqual(variable[5 + i * 3 + j]["cardinality"], 3)
#
# Factors
#
# 5 * 3 LF acc factors + 5 * 3 LF prior factors + 5 class prior factors
self.assertEqual(len(factor), 35)
for i in range(5):
self.assertEqual(factor[i]["factorFunction"], FACTORS["DP_GEN_CLASS_PRIOR"])
self.assertEqual(factor[i]["weightId"], 0)
self.assertEqual(factor[i]["featureValue"], 1)
self.assertEqual(factor[i]["arity"], 1)
self.assertEqual(factor[i]["ftv_offset"], i)
for i in range(5):
for j in range(6):
self.assertEqual(factor[5 + i * 6 + j]["factorFunction"], FACTORS["DP_GEN_LF_ACCURACY"])
self.assertEqual(factor[5 + i * 6 + j]["weightId"], j + 1)
self.assertEqual(factor[5 + i * 6 + j]["featureValue"], 1)
self.assertEqual(factor[5 + i * 6 + j]["arity"], 2)
self.assertEqual(factor[5 + i * 6 + j]["ftv_offset"], 5 + 2 * (i * 6 + j))
#
# Factor to Var
#
self.assertEqual(len(ftv), 65)
# Class prior factor - var edges
for i in range(5):
self.assertEqual(ftv[i]["vid"], i)
self.assertEqual(ftv[i]["dense_equal_to"], 0)
# LF *and LF prior* factor - var edges
for i in range(5):
for j in range(3):
# Each LF has one weight factor and one prior factor here
for k in range(2):
idx = 4 * (i * 3 + j) + 2 * k
self.assertEqual(ftv[5 + idx]["vid"], i)
self.assertEqual(ftv[6 + idx]["vid"], 5 + i * 3 + j)
self.assertEqual(ftv[5 + idx]["dense_equal_to"], 0)
self.assertEqual(ftv[6 + idx]["dense_equal_to"], 0)
#
# Domain mask
#
self.assertEqual(len(domain_mask), 20)
for i in range(20):
self.assertFalse(domain_mask[i])
# n_edges
self.assertEqual(n_edges, 65)
def test_compile_with_deps(self):
# Defines a label matrix
L = sparse.lil_matrix((5, 3))
# The first LF always says yes
L[0, 0] = 1
L[1, 0] = 1
L[2, 0] = 1
L[3, 0] = 1
L[4, 0] = 1
# The second LF votes differently
L[0, 1] = 1
L[2, 1] = -1
L[4, 1] = 1
# The third LF always abstains
# Defined dependencies
deps = []
deps.append((0, 1, DEP_SIMILAR))
deps.append((0, 2, DEP_SIMILAR))
deps.append((0, 1, DEP_FIXING))
deps.append((0, 2, DEP_REINFORCING))
deps.append((1, 2, DEP_EXCLUSIVE))
# Tests compilation
gen_model = GenerativeModel(class_prior=False,
lf_prior=False,
lf_propensity=True,
lf_class_propensity=False)
gen_model._process_dependency_graph(L, deps)
m, n = L.shape
LF_acc_prior_weights = [1.0 for _ in range(n)]
is_fixed = [False for _ in range(n)]
gen_model.cardinality = 2
cardinalities = 2 * np.ones(5)
weight, variable, factor, ftv, domain_mask, n_edges =\
gen_model._compile(L, 0.5, -1.0, LF_acc_prior_weights, is_fixed,
cardinalities)
#
# Weights
#
# Should now be 3 for LFs + 3 fixed for LF priors + 3 for LF propensity
# + 5 for deps
self.assertEqual(len(weight), 14)
# The LF priors
for i in range(0, 6, 2):
self.assertTrue(weight[i]['isFixed'])
self.assertEqual(weight[i]['initialValue'], 1.0)
# The LF weights
for i in range(1, 6, 2):
self.assertFalse(weight[i]['isFixed'])
self.assertEqual(weight[i]['initialValue'], 0.0)
# The dep weights
for i in range(6, 14):
self.assertFalse(weight[i]['isFixed'])
self.assertEqual(weight[i]['initialValue'], 0.5)
#
# Variables
#
self.assertEqual(len(variable), 20)
for i in range(5):
self.assertEqual(variable[i]['isEvidence'], 0)
self.assertTrue(variable[i]['initialValue'] == 0
or variable[i]['initialValue'] == 1)
self.assertEqual(variable[i]["dataType"], 0)
self.assertEqual(variable[i]["cardinality"], 2)
for i in range(5):
for j in range(3):
self.assertEqual(variable[5 + i * 3 + j]['isEvidence'], 1)
# Remap label value; abstain is 0 in L, cardinality (= 2) in NS
if L[i, j] == -1:
l = 0
elif L[i, j] == 0:
l = 2
elif L[i, j] == 1:
l = 1
self.assertEqual(variable[5 + i * 3 + j]['initialValue'], l)
self.assertEqual(variable[5 + i * 3 + j]["dataType"], 0)
self.assertEqual(variable[5 + i * 3 + j]["cardinality"], 3)
#
# Factors
#
self.assertEqual(len(factor), 70)
f_offset = 0
ftv_offset = 0
for i in range(5):
for j in range(6):
self.assertEqual(
factor[f_offset + i * 6 + j]["factorFunction"],
FACTORS["DP_GEN_LF_ACCURACY"])
self.assertEqual(factor[f_offset + i * 6 + j]["weightId"], j)
self.assertEqual(factor[f_offset + i * 6 + j]["featureValue"],
1)
self.assertEqual(factor[f_offset + i * 6 + j]["arity"], 2)
self.assertEqual(factor[f_offset + i * 6 + j]["ftv_offset"],
ftv_offset + 2 * (i * 6 + j))
f_offset = 30
ftv_offset = 60
for i in range(5):
for j in range(3):
self.assertEqual(
factor[f_offset + i * 3 + j]["factorFunction"],
FACTORS["DP_GEN_LF_PROPENSITY"])
self.assertEqual(factor[f_offset + i * 3 + j]["weightId"],
6 + j)
self.assertEqual(factor[f_offset + i * 3 + j]["featureValue"],
1)
self.assertEqual(factor[f_offset + i * 3 + j]["arity"], 1)
self.assertEqual(factor[f_offset + i * 3 + j]["ftv_offset"],
ftv_offset + (i * 3 + j))
f_offset = 45
ftv_offset = 75
for i in range(5):
self.assertEqual(factor[f_offset + i]["factorFunction"],
FACTORS["DP_GEN_DEP_SIMILAR"])
self.assertEqual(factor[f_offset + i]["weightId"], 9)
self.assertEqual(factor[f_offset + i]["featureValue"], 1)
self.assertEqual(factor[f_offset + i]["arity"], 2)
self.assertEqual(factor[f_offset + i]["ftv_offset"],
ftv_offset + 2 * i)
f_offset = 50
ftv_offset = 85
for i in range(5):
self.assertEqual(factor[f_offset + i]["factorFunction"],
FACTORS["DP_GEN_DEP_SIMILAR"])
self.assertEqual(factor[f_offset + i]["weightId"], 10)
self.assertEqual(factor[f_offset + i]["featureValue"], 1)
self.assertEqual(factor[f_offset + i]["arity"], 2)
self.assertEqual(factor[f_offset + i]["ftv_offset"],
ftv_offset + 2 * i)
f_offset = 55
ftv_offset = 95
for i in range(5):
self.assertEqual(factor[f_offset + i]["factorFunction"],
FACTORS["DP_GEN_DEP_FIXING"])
self.assertEqual(factor[f_offset + i]["weightId"], 11)
self.assertEqual(factor[f_offset + i]["featureValue"], 1)
self.assertEqual(factor[f_offset + i]["arity"], 3)
self.assertEqual(factor[f_offset + i]["ftv_offset"],
ftv_offset + 3 * i)
f_offset = 60
ftv_offset = 110
for i in range(5):
self.assertEqual(factor[f_offset + i]["factorFunction"],
FACTORS["DP_GEN_DEP_REINFORCING"])
self.assertEqual(factor[f_offset + i]["weightId"], 12)
self.assertEqual(factor[f_offset + i]["featureValue"], 1)
self.assertEqual(factor[f_offset + i]["arity"], 3)
self.assertEqual(factor[f_offset + i]["ftv_offset"],
ftv_offset + 3 * i)
f_offset = 65
ftv_offset = 125
for i in range(5):
self.assertEqual(factor[f_offset + i]["factorFunction"],
FACTORS["DP_GEN_DEP_EXCLUSIVE"])
self.assertEqual(factor[f_offset + i]["weightId"], 13)
self.assertEqual(factor[f_offset + i]["featureValue"], 1)
self.assertEqual(factor[f_offset + i]["arity"], 2)
self.assertEqual(factor[f_offset + i]["ftv_offset"],
ftv_offset + 2 * i)
#
# Factor to Var
#
self.assertEqual(len(ftv), 135)
ftv_offset = 0
for i in range(5):
for j in range(3):
for k in range(2):
self.assertEqual(
ftv[ftv_offset + 4 * (i * 3 + j) + 2 * k]["vid"], i)
self.assertEqual(
ftv[ftv_offset + 4 * (i * 3 + j) +
2 * k]["dense_equal_to"], 0)
self.assertEqual(
ftv[ftv_offset + 4 * (i * 3 + j) + 2 * k + 1]["vid"],
5 + i * 3 + j)
self.assertEqual(
ftv[ftv_offset + 4 * (i * 3 + j) + 2 * k +
1]["dense_equal_to"], 0)
ftv_offset = 60
for i in range(5):
for j in range(3):
self.assertEqual(ftv[ftv_offset + (i * 3 + j)]["vid"],
5 + i * 3 + j)
self.assertEqual(
ftv[ftv_offset + (i * 3 + j)]["dense_equal_to"], 0)
ftv_offset = 75
for i in range(5):
self.assertEqual(ftv[ftv_offset + 2 * i]["vid"], 5 + i * 3)
self.assertEqual(ftv[ftv_offset + 2 * i]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["vid"], 5 + i * 3 + 1)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["dense_equal_to"], 0)
ftv_offset = 85
for i in range(5):
self.assertEqual(ftv[ftv_offset + 2 * i]["vid"], 5 + i * 3)
self.assertEqual(ftv[ftv_offset + 2 * i]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["vid"], 5 + i * 3 + 2)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["dense_equal_to"], 0)
ftv_offset = 95
for i in range(5):
self.assertEqual(ftv[ftv_offset + 3 * i]["vid"], i)
self.assertEqual(ftv[ftv_offset + 3 * i]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 3 * i + 1]["vid"], 5 + i * 3)
self.assertEqual(ftv[ftv_offset + 3 * i + 1]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 3 * i + 2]["vid"], 5 + i * 3 + 1)
self.assertEqual(ftv[ftv_offset + 3 * i + 2]["dense_equal_to"], 0)
ftv_offset = 110
for i in range(5):
self.assertEqual(ftv[ftv_offset + 3 * i]["vid"], i)
self.assertEqual(ftv[ftv_offset + 3 * i]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 3 * i + 1]["vid"], 5 + i * 3)
self.assertEqual(ftv[ftv_offset + 3 * i + 1]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 3 * i + 2]["vid"], 5 + i * 3 + 2)
self.assertEqual(ftv[ftv_offset + 3 * i + 2]["dense_equal_to"], 0)
ftv_offset = 125
for i in range(5):
self.assertEqual(ftv[ftv_offset + 2 * i]["vid"], 5 + i * 3 + 1)
self.assertEqual(ftv[ftv_offset + 2 * i]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["vid"], 5 + i * 3 + 2)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["dense_equal_to"], 0)
#
# Domain mask
#
self.assertEqual(len(domain_mask), 20)
for i in range(20):
self.assertFalse(domain_mask[i])
# n_edges
self.assertEqual(n_edges, 135)
def test_compile_with_deps(self):
# Defines a label matrix
L = sparse.lil_matrix((5, 3))
# The first LF always says yes
L[0, 0] = 1
L[1, 0] = 1
L[2, 0] = 1
L[3, 0] = 1
L[4, 0] = 1
# The second LF votes differently
L[0, 1] = 1
L[2, 1] = -1
L[4, 1] = 1
# The third LF always abstains
# Defined dependencies
deps = []
deps.append((0, 1, DEP_SIMILAR))
deps.append((0, 2, DEP_SIMILAR))
deps.append((0, 1, DEP_FIXING))
deps.append((0, 2, DEP_REINFORCING))
deps.append((1, 2, DEP_EXCLUSIVE))
# Tests compilation
gen_model = GenerativeModel(class_prior=False, lf_prior=False,
lf_propensity=True, lf_class_propensity=False)
gen_model._process_dependency_graph(L, deps)
m, n = L.shape
LF_acc_prior_weights = [1.0 for _ in range(n)]
is_fixed = [False for _ in range(n)]
gen_model.cardinality = 2
cardinalities = 2 * np.ones(5)
weight, variable, factor, ftv, domain_mask, n_edges =\
gen_model._compile(L, 0.5, -1.0, LF_acc_prior_weights, is_fixed,
cardinalities)
#
# Weights
#
# Should now be 3 for LFs + 3 fixed for LF priors + 3 for LF propensity
# + 5 for deps
self.assertEqual(len(weight), 14)
# The LF priors
for i in range(0,6,2):
self.assertTrue(weight[i]['isFixed'])
self.assertEqual(weight[i]['initialValue'], 1.0)
# The LF weights
for i in range(1,6,2):
self.assertFalse(weight[i]['isFixed'])
self.assertEqual(weight[i]['initialValue'], 0.0)
# The dep weights
for i in range(6, 14):
self.assertFalse(weight[i]['isFixed'])
self.assertEqual(weight[i]['initialValue'], 0.5)
#
# Variables
#
self.assertEqual(len(variable), 20)
for i in range(5):
self.assertEqual(variable[i]['isEvidence'], 0)
self.assertTrue(variable[i]['initialValue'] == 0 or variable[i]['initialValue'] == 1)
self.assertEqual(variable[i]["dataType"], 0)
self.assertEqual(variable[i]["cardinality"], 2)
for i in range(5):
for j in range(3):
self.assertEqual(variable[5 + i * 3 + j]['isEvidence'], 1)
# Remap label value; abstain is 0 in L, cardinality (= 2) in NS
if L[i, j] == -1:
l = 0
elif L[i, j] == 0:
l = 2
elif L[i,j] == 1:
l = 1
self.assertEqual(variable[5 + i * 3 + j]['initialValue'], l)
self.assertEqual(variable[5 + i * 3 + j]["dataType"], 0)
self.assertEqual(variable[5 + i * 3 + j]["cardinality"], 3)
#
# Factors
#
self.assertEqual(len(factor), 70)
f_offset = 0
ftv_offset = 0
for i in range(5):
for j in range(6):
self.assertEqual(factor[f_offset + i * 6+ j]["factorFunction"], FACTORS["DP_GEN_LF_ACCURACY"])
self.assertEqual(factor[f_offset + i * 6 + j]["weightId"], j)
self.assertEqual(factor[f_offset + i * 6 + j]["featureValue"], 1)
self.assertEqual(factor[f_offset + i * 6 + j]["arity"], 2)
self.assertEqual(factor[f_offset + i * 6 + j]["ftv_offset"], ftv_offset + 2 * (i * 6 + j))
f_offset = 30
ftv_offset = 60
for i in range(5):
for j in range(3):
self.assertEqual(factor[f_offset + i * 3 + j]["factorFunction"], FACTORS["DP_GEN_LF_PROPENSITY"])
self.assertEqual(factor[f_offset + i * 3 + j]["weightId"], 6 + j)
self.assertEqual(factor[f_offset + i * 3 + j]["featureValue"], 1)
self.assertEqual(factor[f_offset + i * 3 + j]["arity"], 1)
self.assertEqual(factor[f_offset + i * 3 + j]["ftv_offset"], ftv_offset + (i * 3 + j))
f_offset = 45
ftv_offset = 75
for i in range(5):
self.assertEqual(factor[f_offset + i]["factorFunction"], FACTORS["DP_GEN_DEP_SIMILAR"])
self.assertEqual(factor[f_offset + i]["weightId"], 9)
self.assertEqual(factor[f_offset + i]["featureValue"], 1)
self.assertEqual(factor[f_offset + i]["arity"], 2)
self.assertEqual(factor[f_offset + i]["ftv_offset"], ftv_offset + 2 * i)
f_offset = 50
ftv_offset = 85
for i in range(5):
self.assertEqual(factor[f_offset + i]["factorFunction"], FACTORS["DP_GEN_DEP_SIMILAR"])
self.assertEqual(factor[f_offset + i]["weightId"], 10)
self.assertEqual(factor[f_offset + i]["featureValue"], 1)
self.assertEqual(factor[f_offset + i]["arity"], 2)
self.assertEqual(factor[f_offset + i]["ftv_offset"], ftv_offset + 2 * i)
f_offset = 55
ftv_offset = 95
for i in range(5):
self.assertEqual(factor[f_offset + i]["factorFunction"], FACTORS["DP_GEN_DEP_FIXING"])
self.assertEqual(factor[f_offset + i]["weightId"], 11)
self.assertEqual(factor[f_offset + i]["featureValue"], 1)
self.assertEqual(factor[f_offset + i]["arity"], 3)
self.assertEqual(factor[f_offset + i]["ftv_offset"], ftv_offset + 3 * i)
f_offset = 60
ftv_offset = 110
for i in range(5):
self.assertEqual(factor[f_offset + i]["factorFunction"], FACTORS["DP_GEN_DEP_REINFORCING"])
self.assertEqual(factor[f_offset + i]["weightId"], 12)
self.assertEqual(factor[f_offset + i]["featureValue"], 1)
self.assertEqual(factor[f_offset + i]["arity"], 3)
self.assertEqual(factor[f_offset + i]["ftv_offset"], ftv_offset + 3 * i)
f_offset = 65
ftv_offset = 125
for i in range(5):
self.assertEqual(factor[f_offset + i]["factorFunction"], FACTORS["DP_GEN_DEP_EXCLUSIVE"])
self.assertEqual(factor[f_offset + i]["weightId"], 13)
self.assertEqual(factor[f_offset + i]["featureValue"], 1)
self.assertEqual(factor[f_offset + i]["arity"], 2)
self.assertEqual(factor[f_offset + i]["ftv_offset"], ftv_offset + 2 * i)
#
# Factor to Var
#
self.assertEqual(len(ftv), 135)
ftv_offset = 0
for i in range(5):
for j in range(3):
for k in range(2):
self.assertEqual(ftv[ftv_offset + 4 * (i * 3 + j) + 2 * k]["vid"], i)
self.assertEqual(ftv[ftv_offset + 4 * (i * 3 + j) + 2 * k]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 4 * (i * 3 + j) + 2 * k + 1]["vid"], 5 + i * 3 + j)
self.assertEqual(ftv[ftv_offset + 4 * (i * 3 + j) + 2 * k + 1]["dense_equal_to"], 0)
ftv_offset = 60
for i in range(5):
for j in range(3):
self.assertEqual(ftv[ftv_offset + (i * 3 + j)]["vid"], 5 + i * 3 + j)
self.assertEqual(ftv[ftv_offset + (i * 3 + j)]["dense_equal_to"], 0)
ftv_offset = 75
for i in range(5):
self.assertEqual(ftv[ftv_offset + 2 * i]["vid"], 5 + i * 3)
self.assertEqual(ftv[ftv_offset + 2 * i]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["vid"], 5 + i * 3 + 1)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["dense_equal_to"], 0)
ftv_offset = 85
for i in range(5):
self.assertEqual(ftv[ftv_offset + 2 * i]["vid"], 5 + i * 3)
self.assertEqual(ftv[ftv_offset + 2 * i]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["vid"], 5 + i * 3 + 2)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["dense_equal_to"], 0)
ftv_offset = 95
for i in range(5):
self.assertEqual(ftv[ftv_offset + 3 * i]["vid"], i)
self.assertEqual(ftv[ftv_offset + 3 * i]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 3 * i + 1]["vid"], 5 + i * 3)
self.assertEqual(ftv[ftv_offset + 3 * i + 1]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 3 * i + 2]["vid"], 5 + i * 3 + 1)
self.assertEqual(ftv[ftv_offset + 3 * i + 2]["dense_equal_to"], 0)
ftv_offset = 110
for i in range(5):
self.assertEqual(ftv[ftv_offset + 3 * i]["vid"], i)
self.assertEqual(ftv[ftv_offset + 3 * i]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 3 * i + 1]["vid"], 5 + i * 3)
self.assertEqual(ftv[ftv_offset + 3 * i + 1]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 3 * i + 2]["vid"], 5 + i * 3 + 2)
self.assertEqual(ftv[ftv_offset + 3 * i + 2]["dense_equal_to"], 0)
ftv_offset = 125
for i in range(5):
self.assertEqual(ftv[ftv_offset + 2 * i]["vid"], 5 + i * 3 + 1)
self.assertEqual(ftv[ftv_offset + 2 * i]["dense_equal_to"], 0)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["vid"], 5 + i * 3 + 2)
self.assertEqual(ftv[ftv_offset + 2 * i + 1]["dense_equal_to"], 0)
#
# Domain mask
#
self.assertEqual(len(domain_mask), 20)
for i in range(20):
self.assertFalse(domain_mask[i])
# n_edges
self.assertEqual(n_edges, 135)