def __init__(self):
self.gilp = GurobiILP()
self.weights = FeatureVector()
self.feat_extr = FeatureExtractor()
return
def __init__(self):
self.gilp = GurobiILP()
self.weights = FeatureVector()
self.feat_extr = FeatureExtractor()
return
class Decoder(object):
"""
Implement of decoder for structured prediction
"""
def __init__(self):
self.gilp = GurobiILP()
self.weights = FeatureVector()
self.feat_extr = FeatureExtractor()
return
def decode(self,
instance,
oracle_len='nolen',
node_cost=None,
edge_cost=None):
"""
an instance includes:
my_nodes: (1,) -> AmrNode1, (2,) -> AmrNode2, ...
my_edges: (1,2) -> AmrEdge1, (2,1) -> AmrEdge2,...
selected_nodes: (1,), (3,),... nodes contained in summary graph
selected_edges: (1,2), (3,1),... edges contained in summary graph
"""
logger.debug('start feature extraction...')
curr_filename = instance.filename
my_nodes, oracle_nodes, root_nodes = instance.nodes # nodes and selected nodes
my_edges, oracle_edges = instance.edges # edges and selected edges
num_gold_nodes, num_gold_edges = instance.gold # number of gold nodes and edges
node_weights = {}
edge_weights = {}
# get edge weights
num_nonnegative_edges = 0
for k_edge, v_edge in my_edges.iteritems():
for tag in [0, 1]:
edge_feats = self.feat_extr.getEdgeFeats(
k_edge, v_edge, tag, curr_filename, my_nodes, my_edges)
edge_weights[k_edge + (tag, )] = self.weights.dot(edge_feats)
# cost-augmented decoding
if edge_cost is not None:
if tag == 0 and k_edge not in oracle_edges: # true negative
curr_edge_cost = 0
if tag == 1 and k_edge in oracle_edges: # true positive
curr_edge_cost = 0
if tag == 1 and k_edge not in oracle_edges: # false positive
curr_edge_cost = edge_cost
if tag == 0 and k_edge in oracle_edges: # false negative
curr_edge_cost = edge_cost
edge_weights[k_edge + (tag, )] += curr_edge_cost
if tag == 1 and edge_weights[k_edge + (tag, )] > 0.0:
num_nonnegative_edges += 1
# count number of non-negative edges
logger.debug('[num_nonnegative_edges]: %d' % num_nonnegative_edges)
# get node weights
num_nonnegative_nodes = 0
for k_node, v_node in my_nodes.iteritems():
for tag in [0, 1]:
node_feats = self.feat_extr.getNodeFeats(
k_node, v_node, tag, curr_filename, my_nodes, my_edges)
node_weights[k_node + (tag, )] = self.weights.dot(node_feats)
# cost-augmented decoding
if node_cost is not None:
if tag == 0 and k_node not in oracle_nodes:
curr_node_cost = 0
if tag == 1 and k_node in oracle_nodes:
curr_node_cost = 0
if tag == 1 and k_node not in oracle_nodes: # false positive
curr_node_cost = node_cost
if tag == 0 and k_node in oracle_nodes: # false negative
curr_node_cost = node_cost
node_weights[k_node + (tag, )] += curr_node_cost
if tag == 1 and node_weights[k_node + (tag, )] > 0.0:
num_nonnegative_nodes += 1
# count number of non-negative nodes
logger.debug('[num_nonnegative_nodes]: %d' % num_nonnegative_nodes)
# run Gurobi ILP decoder using node and edge weights
# optionally set the decoded summary length (#nodes and #edges)
logger.debug('start ILP decoding...')
num_selected_nodes = num_gold_nodes if oracle_len == 'nodes' else 0
num_selected_edges = num_gold_edges if oracle_len == 'edges' else 0
selected_nodes, selected_edges, score_pred = self.gilp.decode(
node_weights,
edge_weights,
root_nodes,
num_selected_nodes=num_selected_nodes,
num_selected_edges=num_selected_edges)
logger.debug('[num_gold_nodes]: %d' % num_gold_nodes)
logger.debug('[num_selected_nodes]: %d' % len(selected_nodes))
logger.debug('[num_gold_edges]: %d' % num_gold_edges)
logger.debug('[num_selected_edges]: %d' % len(selected_edges))
# features that are associated with the decoded graph
feat_vec = FeatureVector()
for k_edge, v_edge in my_edges.iteritems():
tag = 1 if k_edge in selected_edges else 0 # use decoded tag
feat_vec += self.feat_extr.getEdgeFeats(k_edge, v_edge, tag,
curr_filename, my_nodes,
my_edges)
for k_node, v_node in my_nodes.iteritems():
tag = 1 if k_node in selected_nodes else 0 # use decoded tag
feat_vec += self.feat_extr.getNodeFeats(k_node, v_node, tag,
curr_filename, my_nodes,
my_edges)
# return features associated with decoded graph
return feat_vec, selected_nodes, selected_edges, score_pred
def oracle(self, instance):
"""
an instance includes:
my_nodes: (1,) -> AmrNode1, (2,) -> AmrNode2, ...
my_edges: (1,2) -> AmrEdge1, (2,1) -> AmrEdge2,...
root_nodes: (1,), (3,),... nodes that are root of sentence
selected_nodes: (1,), (3,),... nodes contained in summary graph
selected_edges: (1,2), (3,1),... edges contained in summary graph
"""
logger.debug('start oracle decoding...')
curr_filename = instance.filename
my_nodes, oracle_nodes, _ = instance.nodes # nodes and selected nodes
my_edges, oracle_edges = instance.edges # edges and selected edges
# features that are associated with oracle graph
feat_vec = FeatureVector()
for k_edge, v_edge in my_edges.iteritems():
tag = 1 if k_edge in oracle_edges else 0 # use oracle tag
feat_vec += self.feat_extr.getEdgeFeats(k_edge, v_edge, tag,
curr_filename, my_nodes,
my_edges)
for k_node, v_node in my_nodes.iteritems():
tag = 1 if k_node in oracle_nodes else 0 # use oracle tag
feat_vec += self.feat_extr.getNodeFeats(k_node, v_node, tag,
curr_filename, my_nodes,
my_edges)
score_true = self.weights.dot(feat_vec)
# return features associated with oracle graph
return feat_vec, oracle_nodes, oracle_edges, score_true
class Decoder(object):
"""
Implement of decoder for structured prediction
"""
def __init__(self):
self.gilp = GurobiILP()
self.weights = FeatureVector()
self.feat_extr = FeatureExtractor()
return
def decode(self, instance, oracle_len='nolen', node_cost=None, edge_cost=None):
"""
an instance includes:
my_nodes: (1,) -> AmrNode1, (2,) -> AmrNode2, ...
my_edges: (1,2) -> AmrEdge1, (2,1) -> AmrEdge2,...
selected_nodes: (1,), (3,),... nodes contained in summary graph
selected_edges: (1,2), (3,1),... edges contained in summary graph
"""
logger.debug('start feature extraction...')
curr_filename = instance.filename
my_nodes, oracle_nodes, root_nodes = instance.nodes # nodes and selected nodes
my_edges, oracle_edges = instance.edges # edges and selected edges
num_gold_nodes, num_gold_edges = instance.gold # number of gold nodes and edges
node_weights = {}
edge_weights = {}
# get edge weights
num_nonnegative_edges = 0
for k_edge, v_edge in my_edges.iteritems():
for tag in [0,1]:
edge_feats = self.feat_extr.getEdgeFeats(k_edge, v_edge, tag, curr_filename, my_nodes, my_edges)
edge_weights[k_edge + (tag,)] = self.weights.dot(edge_feats)
# cost-augmented decoding
if edge_cost is not None:
if tag == 0 and k_edge not in oracle_edges: # true negative
curr_edge_cost = 0
if tag == 1 and k_edge in oracle_edges: # true positive
curr_edge_cost = 0
if tag == 1 and k_edge not in oracle_edges: # false positive
curr_edge_cost = edge_cost
if tag == 0 and k_edge in oracle_edges: # false negative
curr_edge_cost = edge_cost
edge_weights[k_edge + (tag,)] += curr_edge_cost
if tag == 1 and edge_weights[k_edge + (tag,)] > 0.0: num_nonnegative_edges += 1
# count number of non-negative edges
logger.debug('[num_nonnegative_edges]: %d' % num_nonnegative_edges)
# get node weights
num_nonnegative_nodes = 0
for k_node, v_node in my_nodes.iteritems():
for tag in [0,1]:
node_feats = self.feat_extr.getNodeFeats(k_node, v_node, tag, curr_filename, my_nodes, my_edges)
node_weights[k_node + (tag,)] = self.weights.dot(node_feats)
# cost-augmented decoding
if node_cost is not None:
if tag == 0 and k_node not in oracle_nodes:
curr_node_cost = 0
if tag == 1 and k_node in oracle_nodes:
curr_node_cost = 0
if tag == 1 and k_node not in oracle_nodes: # false positive
curr_node_cost = node_cost
if tag == 0 and k_node in oracle_nodes: # false negative
curr_node_cost = node_cost
node_weights[k_node + (tag,)] += curr_node_cost
if tag == 1 and node_weights[k_node + (tag,)] > 0.0: num_nonnegative_nodes += 1
# count number of non-negative nodes
logger.debug('[num_nonnegative_nodes]: %d' % num_nonnegative_nodes)
# run Gurobi ILP decoder using node and edge weights
# optionally set the decoded summary length (#nodes and #edges)
logger.debug('start ILP decoding...')
num_selected_nodes = num_gold_nodes if oracle_len == 'nodes' else 0
num_selected_edges = num_gold_edges if oracle_len == 'edges' else 0
selected_nodes, selected_edges, score_pred = self.gilp.decode(node_weights, edge_weights, root_nodes,
num_selected_nodes=num_selected_nodes,
num_selected_edges=num_selected_edges)
logger.debug('[num_gold_nodes]: %d' % num_gold_nodes)
logger.debug('[num_selected_nodes]: %d' % len(selected_nodes))
logger.debug('[num_gold_edges]: %d' % num_gold_edges)
logger.debug('[num_selected_edges]: %d' % len(selected_edges))
# features that are associated with the decoded graph
feat_vec = FeatureVector()
for k_edge, v_edge in my_edges.iteritems():
tag = 1 if k_edge in selected_edges else 0 # use decoded tag
feat_vec += self.feat_extr.getEdgeFeats(k_edge, v_edge, tag, curr_filename, my_nodes, my_edges)
for k_node, v_node in my_nodes.iteritems():
tag = 1 if k_node in selected_nodes else 0 # use decoded tag
feat_vec += self.feat_extr.getNodeFeats(k_node, v_node, tag, curr_filename, my_nodes, my_edges)
# return features associated with decoded graph
return feat_vec, selected_nodes, selected_edges, score_pred
def oracle(self, instance):
"""
an instance includes:
my_nodes: (1,) -> AmrNode1, (2,) -> AmrNode2, ...
my_edges: (1,2) -> AmrEdge1, (2,1) -> AmrEdge2,...
root_nodes: (1,), (3,),... nodes that are root of sentence
selected_nodes: (1,), (3,),... nodes contained in summary graph
selected_edges: (1,2), (3,1),... edges contained in summary graph
"""
logger.debug('start oracle decoding...')
curr_filename = instance.filename
my_nodes, oracle_nodes, _ = instance.nodes # nodes and selected nodes
my_edges, oracle_edges = instance.edges # edges and selected edges
# features that are associated with oracle graph
feat_vec = FeatureVector()
for k_edge, v_edge in my_edges.iteritems():
tag = 1 if k_edge in oracle_edges else 0 # use oracle tag
feat_vec += self.feat_extr.getEdgeFeats(k_edge, v_edge, tag, curr_filename, my_nodes, my_edges)
for k_node, v_node in my_nodes.iteritems():
tag = 1 if k_node in oracle_nodes else 0 # use oracle tag
feat_vec += self.feat_extr.getNodeFeats(k_node, v_node, tag, curr_filename, my_nodes, my_edges)
score_true = self.weights.dot(feat_vec)
# return features associated with oracle graph
return feat_vec, oracle_nodes, oracle_edges, score_true
