def print_alignments(align_count,
title,
graph_pair1,
graph_pair2,
graphs,
nodes_list,
out=stdout):
if nodes_list:
header(title, out, char="-")
for nodes in nodes_list:
align_count += 1
rel1 = str(graph_pair1.get_align(nodes))
rel2 = str(graph_pair2.get_align(nodes))
# tricky because of implicit coercions,
# see "Formatting Markers" http://www.python.org/dev/peps/pep-0100/
print >> out, "#%d:" % align_count
s = '(%s) %s [%s:%s]: "%s"' % (
nodes.source,
graphs.source.node[nodes.source]["label"].encode("utf-8"),
graphs.source.node[nodes.source]["begin"],
graphs.source.node[nodes.source]["end"],
graphs.source.get_node_token_string(nodes.source))
print >> out, s.encode("utf-8")
print >> out, "<<<", rel1.upper(), "/", rel2.upper(), ">>>"
s = '(%s) %s [%s:%s]: "%s"\n' % (
nodes.target, graphs.target.node[nodes.target]["label"],
graphs.target.node[nodes.target]["begin"],
graphs.target.node[nodes.target]["end"],
graphs.target.get_node_token_string(nodes.target))
print >> out, s.encode("utf-8")
return align_count
def print_alignments(align_count, title, graph_pair1, graph_pair2, graphs,
nodes_list, out=stdout):
if nodes_list:
header(title, out, char="-")
for nodes in nodes_list:
align_count += 1
rel1 = str(graph_pair1.get_align(nodes))
rel2 = str(graph_pair2.get_align(nodes))
# tricky because of implicit coercions,
# see "Formatting Markers" http://www.python.org/dev/peps/pep-0100/
print >>out, "#%d:" % align_count
s = '(%s) %s [%s:%s]: "%s"' % (
nodes.source,
graphs.source.node[nodes.source]["label"].encode("utf-8"),
graphs.source.node[nodes.source]["begin"],
graphs.source.node[nodes.source]["end"],
graphs.source.get_node_token_string(nodes.source))
print >>out, s.encode("utf-8")
print >>out, "<<<", rel1.upper(), "/", rel2.upper(), ">>>"
s = '(%s) %s [%s:%s]: "%s"\n' % (
nodes.target,
graphs.target.node[nodes.target]["label"],
graphs.target.node[nodes.target]["begin"],
graphs.target.node[nodes.target]["end"],
graphs.target.get_node_token_string(nodes.target))
print >>out, s.encode("utf-8")
return align_count
def write_alignment_per_relation(self, out=stdout):
"""
write evaluation of alignment for each relation separtately
"""
header("Alignment per relation", out)
for rel in self.relations:
self[rel].write(self.names, out=out, heading=rel.upper())
def write_alignment_per_relation(self, out=stdout):
"""
write evaluation of alignment for each relation separtately
"""
header("Alignment per relation", out)
for rel in self.relations:
self[rel].write(self.names, out=out, heading=rel.upper())
def print_comments(graph_pair, annot, out, encoding="utf-8"):
try:
comment = graph_pair.get_meta_data().find("comment").text
except AttributeError:
return
if comment.strip():
header("Comments by " + annot, out, char="-")
print >> out, comment.encode(encoding), "\n"
def print_comments(graph_pair, annot, out, encoding="utf-8"):
try:
comment = graph_pair.get_meta_data().find("comment").text
except AttributeError:
return
if comment.strip():
header("Comments by " + annot, out, char="-")
print >>out, comment.encode(encoding), "\n"
def weight(setting):
"""
Weight predictions
@param setting: Setting instance specifying the experimental setting
"""
if setting.weight:
log.info("\n" + header("WEIGHT STEP"))
if setting.develop:
weight_files(
setting.dev_inst_fns,
setting.dev_clas_fns,
setting.weight_func,
descriptor=setting.descriptor,
n=setting.n,
binary=setting.binary)
if setting.validate:
weight_files(
setting.val_inst_fns,
setting.val_clas_fns,
setting.weight_func,
descriptor=setting.descriptor,
n=setting.n,
binary=setting.binary)
def extract(setting):
"""
Extract features from corpus files, producing instance files and true
corpus files.
@param setting: Setting instance specifying the experimental setting
"""
if setting.extract:
log.info("\n" + header("EXTRACT STEP"))
makedirs(setting.inst_dir)
makedirs(setting.true_dir)
if setting.develop:
inst_fns = setting.make_inst_fns(setting.dev_part_fns)
true_fns = setting.make_true_fns(setting.dev_part_fns)
extract_files(setting.extractor,
setting.graph_selector,
setting.dev_part_fns,
inst_fns,
true_fns,
binary=setting.binary)
if setting.validate:
inst_fns = setting.make_inst_fns(setting.val_part_fns)
true_fns = setting.make_true_fns(setting.val_part_fns)
extract_files(setting.extractor,
setting.graph_selector,
setting.val_part_fns,
inst_fns,
true_fns,
binary=setting.binary)
def exp_init(setting):
log.info("\n" + header("INIT"))
log.info("Setting at start:\n" + str(setting) + "\n")
buf = StringIO.StringIO()
buf.write("feature description:\n")
setting.descriptor.pprint(buf)
log.info(buf.getvalue())
def evaluate(setting):
"""
Evaluate development data
@param setting: Setting instance specifying the experimental setting
"""
if setting.evaluate:
log.info("\n" + header("EVALUATE STEP"))
makedirs(setting.eval_dir)
if setting.develop:
setting.dev_eval = eval_files(
setting.dev_true_fns,
setting.dev_pred_fns,
setting.dev_eval_fname,
align_eval=setting.evaluator,
n=setting.n)
if setting.validate:
setting.val_eval = eval_files(
setting.val_true_fns,
setting.val_pred_fns,
setting.val_eval_fname,
align_eval=setting.evaluator,
n=setting.n)
def extract(setting):
"""
Extract features from corpus files, producing instance files and true
corpus files.
@param setting: Setting instance specifying the experimental setting
"""
if setting.extract:
log.info("\n" + header("EXTRACT STEP"))
makedirs(setting.inst_dir)
makedirs(setting.true_dir)
if setting.develop:
inst_fns = setting.make_inst_fns(setting.dev_part_fns)
true_fns = setting.make_true_fns(setting.dev_part_fns)
extract_files(
setting.extractor,
setting.graph_selector,
setting.dev_part_fns,
inst_fns,
true_fns,
binary=setting.binary)
if setting.validate:
inst_fns = setting.make_inst_fns(setting.val_part_fns)
true_fns = setting.make_true_fns(setting.val_part_fns)
extract_files(
setting.extractor,
setting.graph_selector,
setting.val_part_fns,
inst_fns,
true_fns,
binary=setting.binary)
def merge(setting):
"""
Merge data
@param setting: Setting instance specifying the experimental setting
"""
if setting.merge:
log.info("\n" + header("MERGE STEP"))
makedirs(setting.pred_dir)
if setting.develop:
pred_fns = setting.make_pred_fns(setting.dev_true_fns)
merge_files(
setting.dev_inst_fns,
setting.dev_true_fns,
pred_fns,
merger=setting.merger,
descriptor=setting.descriptor,
n=setting.n,
binary=setting.binary)
if setting.validate:
pred_fns = setting.make_pred_fns(setting.val_true_fns)
merge_files(
setting.val_inst_fns,
setting.val_true_fns,
pred_fns,
merger=setting.merger,
descriptor=setting.descriptor,
n=setting.n,
binary=setting.binary)
def exp_init(setting):
log.info("\n" + header("INIT"))
log.info("Setting at start:\n" + str(setting) + "\n")
buf = StringIO.StringIO()
buf.write("feature description:\n")
setting.descriptor.pprint(buf)
log.info(buf.getvalue())
def sample(setting):
"""
Sample training data
@param setting: Setting instance specifying the experimental setting
"""
if setting.sample:
log.info("\n" + header("SAMPLE STEP"))
makedirs(setting.samp_dir)
if setting.develop:
samp_fns = setting.make_samp_fns(setting.dev_inst_fns)
sample_file(setting.class_fracts, setting.dev_inst_fns, samp_fns)
if setting.validate:
samp_fns = setting.make_samp_fns(setting.val_inst_fns)
sample_file(setting.class_fracts, setting.val_inst_fns, samp_fns)
def sample(setting):
"""
Sample training data
@param setting: Setting instance specifying the experimental setting
"""
if setting.sample:
log.info("\n" + header("SAMPLE STEP"))
makedirs(setting.samp_dir)
if setting.develop:
samp_fns = setting.make_samp_fns(setting.dev_inst_fns)
sample_file(setting.class_fracts, setting.dev_inst_fns, samp_fns)
if setting.validate:
samp_fns = setting.make_samp_fns(setting.val_inst_fns)
sample_file(setting.class_fracts, setting.val_inst_fns, samp_fns)
def create_parts(setting):
"""
Create the parallel graph corpora constituting the data parts for
development and validation
@param setting: Setting instance specifying the experimental setting
"""
if setting.part:
log.info("\n" + header("PARTING STEP"))
if setting.develop:
create_part_files(
setting.dev_parts,
base_dir=setting.corpus_dir,
part_dir=setting.part_dir,
max_size=setting.part_max_size)
if setting.validate:
create_part_files(
setting.val_parts,
base_dir=setting.corpus_dir,
part_dir=setting.part_dir,
max_size=setting.part_max_size)
def match(setting):
"""
Match data
@param setting: Setting instance specifying the experimental setting
"""
if setting.match:
log.info("\n" + header("MATCH STEP"))
if setting.develop:
match_files(
setting.dev_inst_fns,
setting.matcher,
descriptor=setting.descriptor,
n=setting.n,
binary=setting.binary)
if setting.validate:
match_files(
setting.val_inst_fns,
setting.matcher,
descriptor=setting.descriptor,
n=setting.n,
binary=setting.binary)
def classify(setting):
"""
Classify corpus instances
@param setting: Setting instance specifying the experimental setting
"""
if setting.classify:
log.info("\n" + header("CLASSIFY STEP"))
makedirs(setting.clas_dir)
if setting.train_sample:
train_inst_fns = setting.dev_samp_fns
else:
train_inst_fns = setting.dev_inst_fns
if setting.develop:
classify_file_cv(
train_inst_fns,
test_inst_fns=setting.dev_inst_fns,
out_fns=setting.make_out_fns(setting.dev_inst_fns),
log_fns=setting.make_log_fns(setting.dev_inst_fns),
descriptor=setting.descriptor,
timbl=setting.classifier,
options=setting.timbl_opts,
n=setting.n,
log=setting.timbl_log)
if setting.validate:
classify_file(
train_inst_fns,
setting.val_inst_fns,
out_fns=setting.make_out_fns(setting.val_inst_fns),
log_fn=setting.make_log_fname(setting.val_inst_fns[0]),
descriptor=setting.descriptor,
timbl=setting.classifier,
options=setting.timbl_opts,
log=setting.timbl_log)
def classify(setting):
"""
Classify corpus instances
@param setting: Setting instance specifying the experimental setting
"""
if setting.classify:
log.info("\n" + header("CLASSIFY STEP"))
makedirs(setting.clas_dir)
if setting.train_sample:
train_inst_fns = setting.dev_samp_fns
else:
train_inst_fns = setting.dev_inst_fns
if setting.develop:
classify_file_cv(
train_inst_fns,
test_inst_fns=setting.dev_inst_fns,
out_fns=setting.make_out_fns(setting.dev_inst_fns),
log_fns=setting.make_log_fns(setting.dev_inst_fns),
descriptor=setting.descriptor,
timbl=setting.classifier,
options=setting.timbl_opts,
n=setting.n,
log=setting.timbl_log)
if setting.validate:
classify_file(train_inst_fns,
setting.val_inst_fns,
out_fns=setting.make_out_fns(setting.val_inst_fns),
log_fn=setting.make_log_fname(
setting.val_inst_fns[0]),
descriptor=setting.descriptor,
timbl=setting.classifier,
options=setting.timbl_opts,
log=setting.timbl_log)
def write_alignment_overall(self, out=stdout, percent=True):
"""
write evaluation summary of alignment over all relations
"""
width = 14
separator = 4 * width * "-" + "\n"
header("Alignment over all relations", out)
# write counts
out.write("Relation:".ljust(width))
for c in AlignCounts.count_keys:
c = c.capitalize() + ":"
out.write(c.rjust(width))
out.write('\n' + separator)
for rel, align_counts in zip(self.relations, self):
out.write(rel.ljust(width))
for c in AlignCounts.count_keys:
s = str(self[rel].count_stats[c]["sum"])
out.write(s.rjust(width))
out.write('\n')
out.write(separator)
out.write("Sum:".ljust(width))
for k in AlignCounts.count_keys:
s = str(self.count_stats[k]["sum"])
out.write(s.rjust(width))
out.write('\n\n\n')
# write measures
out.write("Relation:".ljust(width))
for c in AlignCounts.measure_keys:
c = c.capitalize() + ":"
out.write(c.rjust(width))
out.write('\n' + separator)
for rel, align_counts in zip(self.relations, self):
out.write(rel.ljust(width))
for m in AlignCounts.measure_keys:
# repport the relation's micro mean here
value = self[rel].measure_stats[m]["micro"]["mean"]
if percent: value *= 100
s = "%.2f" % value
out.write(s.rjust(width))
out.write('\n')
out.write(separator)
for method in self.measure_stat_methods:
for stat in self.measure_stat_keys:
s = method.capitalize()+ " " + stat.capitalize() + ":"
out.write(s.ljust(width))
for m in AlignCounts.measure_keys:
value = self.measure_stats[m][method][stat]
if percent: value *= 100
s = "%.2f" % value
out.write(s.rjust(width))
out.write('\n')
out.write('\n\n')
def write_alignment_only(self, out=stdout):
"""
write evaluation of alignment only, irrespective of relation labels
"""
header("Alignment only (regardlless of relation)", out)
self[AlignEval.any_rel].write(self.names, out=out)
def write_alignment_only(self, out=stdout):
"""
write evaluation of alignment only, irrespective of relation labels
"""
header("Alignment only (regardlless of relation)", out)
self[AlignEval.any_rel].write(self.names, out=out)
def write_alignment_overall(self, out=stdout, percent=True):
"""
write evaluation summary of alignment over all relations
"""
width = 14
separator = 4 * width * "-" + "\n"
header("Alignment over all relations", out)
# write counts
out.write("Relation:".ljust(width))
for c in AlignCounts.count_keys:
c = c.capitalize() + ":"
out.write(c.rjust(width))
out.write('\n' + separator)
for rel, align_counts in zip(self.relations, self):
out.write(rel.ljust(width))
for c in AlignCounts.count_keys:
s = str(self[rel].count_stats[c]["sum"])
out.write(s.rjust(width))
out.write('\n')
out.write(separator)
out.write("Sum:".ljust(width))
for k in AlignCounts.count_keys:
s = str(self.count_stats[k]["sum"])
out.write(s.rjust(width))
out.write('\n\n\n')
# write measures
out.write("Relation:".ljust(width))
for c in AlignCounts.measure_keys:
c = c.capitalize() + ":"
out.write(c.rjust(width))
out.write('\n' + separator)
for rel, align_counts in zip(self.relations, self):
out.write(rel.ljust(width))
for m in AlignCounts.measure_keys:
# repport the relation's micro mean here
value = self[rel].measure_stats[m]["micro"]["mean"]
if percent: value *= 100
s = "%.2f" % value
out.write(s.rjust(width))
out.write('\n')
out.write(separator)
for method in self.measure_stat_methods:
for stat in self.measure_stat_keys:
s = method.capitalize() + " " + stat.capitalize() + ":"
out.write(s.ljust(width))
for m in AlignCounts.measure_keys:
value = self.measure_stats[m][method][stat]
if percent: value *= 100
s = "%.2f" % value
out.write(s.rjust(width))
out.write('\n')
out.write('\n\n')
def pgc_diff(corpus1, corpus2,
corpus_name1="Corpus1", corpus_name2="Corpus2",
annot1="Annot1", annot2="Annot2",
words_only=False,
show_comments=False,
show_ident=False,
relations=None,
out=stdout):
"""
reports the differences (and optionally the similarities) between
the labeled alignments from two parallel graph corpora
"""
assert len(corpus1) == len(corpus2)
if not relations:
relations = corpus1.get_relations()
# counter for numbering the alignments when printing;
# may be less than the actual number of alignments when identical alignments
# are not printed (cf. show_ident option)
align_count = 0
# counter for numbering the graph pairs when printing
pair_count = 0
header("%s corpus: %s\n%s corpus: %s" % (annot1, corpus_name1, annot2,
corpus_name2), width=120, char="#")
for graph_pair1, graph_pair2 in zip(corpus1, corpus2):
# assume that the corpora have the same graph pairs in the same order,
# so the only difference is in the aligned nodes
assert graph_pair1._banks == graph_pair2._banks
assert graph_pair1._graphs_equal(graph_pair2)
pair_count += 1
ident = []
rel_diff = []
uniq1 = []
uniq2 = []
# recall that graphs are identical
graphs = graph_pair1.get_graphs()
for nodes, rel1 in graph_pair1.alignments_iter(relations=relations):
if ( words_only and
graphs.source.node_node_is_non_terminal(nodes.source) and
graphs.target.node_is_non_terminal(nodes.target) ):
continue
rel2 = graph_pair2.get_align(nodes)
if not rel2:
uniq1.append(nodes)
elif rel1 == rel2:
ident.append(nodes)
else:
rel_diff.append(nodes)
for nodes, rel2 in graph_pair2.alignments_iter(relations=relations):
if ( words_only and
( graphs.source.node_is_terminal(nodes.source) or
graphs.target.node_is_terminal(nodes.target) )):
continue
if not graph_pair1.get_align(nodes):
uniq2.append(nodes)
#if not ( ident and rel_diff and uniq1 and uniq2 and show_comments ):
# continue
header("Graph pair %d" % pair_count, width=120, char="=")
print >>out, graphs.source.get_graph_token_string().encode("utf-8"), "\n"
print >>out, graphs.target.get_graph_token_string().encode("utf-8"), "\n"
if show_comments:
print_comments(graph_pair1, annot1, out)
print_comments(graph_pair2, annot2, out)
if show_ident:
ident.sort(cmp=cmp_nodes)
align_count = print_alignments(align_count, "Identical",
graph_pair1, graph_pair2, graphs, ident, out)
rel_diff.sort(cmp=cmp_nodes)
align_count = print_alignments(align_count, "Relation different",
graph_pair1, graph_pair2, graphs, rel_diff, out)
uniq1.sort(cmp=cmp_nodes)
align_count = print_alignments(align_count, annot1 + " only",
graph_pair1, graph_pair2, graphs, uniq1, out)
uniq2.sort(cmp=cmp_nodes)
align_count = print_alignments(align_count, annot2 + " only",
graph_pair1, graph_pair2, graphs, uniq2, out)
def pgc_diff(corpus1,
corpus2,
corpus_name1="Corpus1",
corpus_name2="Corpus2",
annot1="Annot1",
annot2="Annot2",
words_only=False,
show_comments=False,
show_ident=False,
relations=None,
out=stdout):
"""
reports the differences (and optionally the similarities) between
the labeled alignments from two parallel graph corpora
"""
assert len(corpus1) == len(corpus2)
if not relations:
relations = corpus1.get_relations()
# counter for numbering the alignments when printing;
# may be less than the actual number of alignments when identical alignments
# are not printed (cf. show_ident option)
align_count = 0
# counter for numbering the graph pairs when printing
pair_count = 0
header("%s corpus: %s\n%s corpus: %s" %
(annot1, corpus_name1, annot2, corpus_name2),
width=120,
char="#")
for graph_pair1, graph_pair2 in zip(corpus1, corpus2):
# assume that the corpora have the same graph pairs in the same order,
# so the only difference is in the aligned nodes
assert graph_pair1._banks == graph_pair2._banks
assert graph_pair1._graphs_equal(graph_pair2)
pair_count += 1
ident = []
rel_diff = []
uniq1 = []
uniq2 = []
# recall that graphs are identical
graphs = graph_pair1.get_graphs()
for nodes, rel1 in graph_pair1.alignments_iter(relations=relations):
if (words_only
and graphs.source.node_node_is_non_terminal(nodes.source)
and graphs.target.node_is_non_terminal(nodes.target)):
continue
rel2 = graph_pair2.get_align(nodes)
if not rel2:
uniq1.append(nodes)
elif rel1 == rel2:
ident.append(nodes)
else:
rel_diff.append(nodes)
for nodes, rel2 in graph_pair2.alignments_iter(relations=relations):
if (words_only
and (graphs.source.node_is_terminal(nodes.source)
or graphs.target.node_is_terminal(nodes.target))):
continue
if not graph_pair1.get_align(nodes):
uniq2.append(nodes)
#if not ( ident and rel_diff and uniq1 and uniq2 and show_comments ):
# continue
header("Graph pair %d" % pair_count, width=120, char="=")
print >> out, graphs.source.get_graph_token_string().encode(
"utf-8"), "\n"
print >> out, graphs.target.get_graph_token_string().encode(
"utf-8"), "\n"
if show_comments:
print_comments(graph_pair1, annot1, out)
print_comments(graph_pair2, annot2, out)
if show_ident:
ident.sort(cmp=cmp_nodes)
align_count = print_alignments(align_count, "Identical",
graph_pair1, graph_pair2, graphs,
ident, out)
rel_diff.sort(cmp=cmp_nodes)
align_count = print_alignments(align_count, "Relation different",
graph_pair1, graph_pair2, graphs,
rel_diff, out)
uniq1.sort(cmp=cmp_nodes)
align_count = print_alignments(align_count, annot1 + " only",
graph_pair1, graph_pair2, graphs, uniq1,
out)
uniq2.sort(cmp=cmp_nodes)
align_count = print_alignments(align_count, annot2 + " only",
graph_pair1, graph_pair2, graphs, uniq2,
out)
def exp_dev_fast(setting):
"""
perform a fast alignment experiment on development data
Weighting, matching and merging takes place per test corpus without
writing intermediary results to a file.
"""
assert setting.develop and not setting.validate
exp_init(setting)
create_parts(setting)
# It's impossible to do extraction one corpus a time, because in order to
# classify a test corpus you need instances for all the other training
# corpora! Moreover, since Timbl classification is file-based, we need to
# write the corpus instance files to disk. These files can be huge and
# keeping all of them in memory seems to offer little benefit.
extract(setting)
sample(setting)
# Timbl writes its output to a file, which then needs to be parsed in
# order to insert the class predictions and weights into the corpus
# instances. That means there is no advantage to doing classification
# one corpus a time.
classify(setting)
log.info("\n" + header("WEIGHT/MATCH/MERGE STEP"))
# reset evaluator
if setting.evaluate: setting.evaluator.__init__()
scope = zip(setting.dev_inst_fns, setting.dev_clas_fns,
setting.dev_true_fns)[:setting.n]
for inst_fname, out_fname, true_fname in scope:
log.info("reading corpus instances {0}".format(inst_fname))
corpus_inst = CorpusInst()
corpus_inst.loadtxt(inst_fname, setting.descriptor.dtype)
if setting.weight:
log.info("reading classifier output {0}".format(out_fname))
timbl_out = parse_timbl_output(open(out_fname))
log.info("weighting...")
weight_corpus(corpus_inst, timbl_out, setting.weight_func)
if setting.match:
log.info("matching...")
match_corpus(corpus_inst, setting.matcher)
if setting.merge:
log.info("reading true corpus {0}".format(true_fname))
true_corpus = ParallelGraphCorpus(inf=true_fname,
graph_loading=LOAD_NONE)
log.info("merging...")
pred_corpus = merge_corpus(corpus_inst, true_corpus,
setting.merger)
if setting.evaluate:
name = os.path.basename(true_fname).split("_")[0]
setting.evaluator.add(true_corpus, pred_corpus, name)
if setting.evaluate:
log.info("evaluting...")
setting.evaluator.run_eval()
log.info("saving evaluation {0}".format(setting.dev_eval_fname))
makedirs(setting.eval_dir)
setting.evaluator.write(setting.dev_eval_fname)
exp_exit(setting)
def exp_exit(setting):
log.info("\n" + header("EXIT"))
pickle(setting)
feat_weight_graphs(setting)
log.info("Setting at end:\n" + str(setting) + "\n")
def exp_dev_fast(setting):
"""
perform a fast alignment experiment on development data
Weighting, matching and merging takes place per test corpus without
writing intermediary results to a file.
"""
assert setting.develop and not setting.validate
exp_init(setting)
create_parts(setting)
# It's impossible to do extraction one corpus a time, because in order to
# classify a test corpus you need instances for all the other training
# corpora! Moreover, since Timbl classification is file-based, we need to
# write the corpus instance files to disk. These files can be huge and
# keeping all of them in memory seems to offer little benefit.
extract(setting)
sample(setting)
# Timbl writes its output to a file, which then needs to be parsed in
# order to insert the class predictions and weights into the corpus
# instances. That means there is no advantage to doing classification
# one corpus a time.
classify(setting)
log.info("\n" + header("WEIGHT/MATCH/MERGE STEP"))
# reset evaluator
if setting.evaluate:
setting.evaluator.__init__()
scope = zip(setting.dev_inst_fns, setting.dev_clas_fns, setting.dev_true_fns)[: setting.n]
for inst_fname, out_fname, true_fname in scope:
log.info("reading corpus instances {0}".format(inst_fname))
corpus_inst = CorpusInst()
corpus_inst.loadtxt(inst_fname, setting.descriptor.dtype)
if setting.weight:
log.info("reading classifier output {0}".format(out_fname))
timbl_out = parse_timbl_output(open(out_fname))
log.info("weighting...")
weight_corpus(corpus_inst, timbl_out, setting.weight_func)
if setting.match:
log.info("matching...")
match_corpus(corpus_inst, setting.matcher)
if setting.merge:
log.info("reading true corpus {0}".format(true_fname))
true_corpus = ParallelGraphCorpus(inf=true_fname, graph_loading=LOAD_NONE)
log.info("merging...")
pred_corpus = merge_corpus(corpus_inst, true_corpus, setting.merger)
if setting.evaluate:
name = os.path.basename(true_fname).split("_")[0]
setting.evaluator.add(true_corpus, pred_corpus, name)
if setting.evaluate:
log.info("evaluting...")
setting.evaluator.run_eval()
log.info("saving evaluation {0}".format(setting.dev_eval_fname))
makedirs(setting.eval_dir)
setting.evaluator.write(setting.dev_eval_fname)
exp_exit(setting)
def exp_exit(setting):
log.info("\n" + header("EXIT"))
pickle(setting)
feat_weight_graphs(setting)
log.info("Setting at end:\n" + str(setting) + "\n")