def __projection_based_derivation_tree(self, la, variational=False, op=prod):
manager = PyDerivationManager(self.grammar, self.nontMap)
derivations = [der for _, der in self.base_parser.k_best_derivation_trees()]
manager.convert_derivations_to_hypergraph(derivations)
manager.set_io_cycle_limit(200)
manager.set_io_precision(0.000001)
self.debug = False
self.log_mode = True
edge_weights = py_edge_weight_projection(la, manager, variational=variational, debug=self.debug,
log_mode=self.log_mode)
der = manager.viterbi_derivation(0, edge_weights, self.grammar, op=op, log_mode=self.log_mode)
if der is None:
if True or self.debug:
nans = 0
infs = 0
zeros = 0
for weight in edge_weights:
if math.isnan(weight):
nans += 1
if math.isinf(weight):
infs += 1
if weight == 0.0:
zeros += 1
print("[", len(edge_weights), nans, infs, zeros, "]")
if len(edge_weights) < 200:
print("orig:", edge_weights)
edge_weights = py_edge_weight_projection(la, manager, variational=variational, debug=True, log_mode=self.log_mode)
print("1:", edge_weights)
edge_weights = py_edge_weight_projection(la, manager, variational=variational, debug=True,
log_mode=self.log_mode)
print("2:", edge_weights)
print("p", end="")
_, der = next(self.k_best_derivation_trees())
return der
def __projection_based_derivation_tree(self, la, variational=False, op=prod):
if self.nontMap is None:
print("A nonterminal map is required for weight projection based parsing!")
return None
manager = PyDerivationManager(self.grammar, self.nontMap)
manager.convert_chart_to_hypergraph(self.chart, self.disco_grammar, debug=False)
if self.grammarInfo is not None:
assert manager.is_consistent_with_grammar(self.grammarInfo)
manager.set_io_cycle_limit(200)
manager.set_io_precision(0.000001)
if not isinstance(la, list):
la = [la]
edge_weights = None
for l in la:
edge_weights_l = py_edge_weight_projection(l, manager, variational=variational, debug=self.debug,
log_mode=self.log_mode)
if edge_weights is None:
edge_weights = edge_weights_l
else:
if self.log_mode:
edge_weights = [w1 + w2 for w1, w2 in zip(edge_weights, edge_weights_l)]
else:
edge_weights = [op(w1, w2) for w1, w2 in zip(edge_weights, edge_weights_l)]
if self.debug:
nans = 0
infs = 0
zeros = 0
for weight in edge_weights:
if weight == float("nan"):
nans += 1
if weight == float("inf") or weight == float("-inf"):
infs += 1
if weight == 0.0:
zeros += 1
print("[", len(edge_weights), nans, infs, zeros, "]")
if len(edge_weights) < 100:
print(edge_weights)
der = manager.viterbi_derivation(0, edge_weights, self.grammar, op=op, log_mode=self.log_mode)
if der is None:
print("p", end="")
der = self.latent_viterbi_derivation(debug=self.debug)
if der is not None:
der = LCFRSDerivationWrapper(der)
if der is None:
_, der = next(self.k_best_derivation_trees())
return der
