def run(self):
"""Perform the evaluation"""
logging.info("Starting workflow %r run", self)
all_results = {}
evaluator = self.create_evaluator()
count = 0
for each_article in self._reader:
count += 1
logging.info("Working on article %d: %r", count, each_article)
if not self.include_article(each_article):
logging.log(
ULTRADEBUG, "Skipping article %r due to exclusion "
" criteria.", each_article)
continue
try:
ranked_article = self.graph_and_rank(each_article)
except CouldNotRank:
continue
converted_terms = self.convert(ranked_article)
cut_terms = converted_terms.terms_higher_than_or_equal_to(
self._ranking_cutoff)
logging.debug(
"Lowest-ranking term is term #%d out of %d"
" (score=%1.5f, highest score=%1.5f)", len(cut_terms),
len(converted_terms), [x[1] for x in cut_terms][-1],
[x[1] for x in cut_terms][0])
medline_record_mesh_terms = ExpressionList().from_medline(
each_article.set_id.article_record().mesh_headings)
flat_medline = medline_record_mesh_terms.flatten()
flattened_terms = self.flatten_generated_terms(
flat_medline, cut_terms)
flattened_terms = self.limit_length(flat_medline, flattened_terms)
if len(flat_medline) == 0:
logging.warn(
"No gold standard available for article %r. "
"Omitting it from the result set.", each_article)
continue
eval_result = self.perform_evaluation(each_article, evaluator,
flat_medline,
flattened_terms)
flattened_major_headings=\
medline_record_mesh_terms.major_headings()
logging.debug("Original headings: %r Major headings: %r",
medline_record_mesh_terms, flattened_major_headings)
mh_result_temp = self.perform_evaluation(each_article, evaluator,
flattened_major_headings,
flattened_terms)
mh_result = NamedResultSet("mh_", mh_result_temp)
# Compute the total recall, too
total_recall = self.compute_total_recall(flat_medline,
converted_terms)
eval_result.add(total_recall)
# Unify the result sets
all_results[each_article.set_id] = eval_result | mh_result
logging.info("Writing out results.")
self.output(all_results)
self.output_metadata()
return
def run(self):
"""Perform the evaluation"""
logging.info("Starting workflow %r run", self)
all_results={}
evaluator=self.create_evaluator()
count=0
for each_article in self._reader:
count+=1
logging.info("Working on article %d: %r", count, each_article)
if not self.include_article(each_article):
logging.log(ULTRADEBUG, "Skipping article %r due to exclusion "
" criteria.", each_article)
continue
try:
ranked_article=self.graph_and_rank(each_article)
except CouldNotRank:
continue
converted_terms=self.convert(ranked_article)
cut_terms=converted_terms.terms_higher_than_or_equal_to(
self._ranking_cutoff)
logging.debug("Lowest-ranking term is term #%d out of %d"
" (score=%1.5f, highest score=%1.5f)",
len(cut_terms), len(converted_terms),
[x[1] for x in cut_terms][-1],
[x[1] for x in cut_terms][0])
medline_record_mesh_terms=ExpressionList().from_medline(
each_article.set_id.article_record().mesh_headings)
flat_medline=medline_record_mesh_terms.flatten()
flattened_terms=self.flatten_generated_terms(flat_medline,
cut_terms)
flattened_terms=self.limit_length(flat_medline, flattened_terms)
if len(flat_medline)==0:
logging.warn("No gold standard available for article %r. "
"Omitting it from the result set.", each_article)
continue
eval_result=self.perform_evaluation(each_article,
evaluator,
flat_medline,
flattened_terms)
flattened_major_headings=\
medline_record_mesh_terms.major_headings()
logging.debug("Original headings: %r Major headings: %r",
medline_record_mesh_terms,
flattened_major_headings)
mh_result_temp=self.perform_evaluation(each_article, evaluator,
flattened_major_headings,
flattened_terms)
mh_result=NamedResultSet("mh_", mh_result_temp)
# Compute the total recall, too
total_recall=self.compute_total_recall(flat_medline,
converted_terms)
eval_result.add(total_recall)
# Unify the result sets
all_results[each_article.set_id]=eval_result | mh_result
logging.info("Writing out results.")
self.output(all_results)
self.output_metadata()
return
def process_article(self, each_article):
if not self.include_article(each_article):
logging.log(ULTRADEBUG, "Skipping article %r due to exclusion "
" criteria.", each_article)
return
try:
ranked_article=self.graph_and_rank(each_article)
except CouldNotRank:
return
logging.debug("Ranked article: %r", ranked_article)
converted_terms=self.convert(ranked_article)
logging.debug("Converted terms: %r", converted_terms)
cut_terms=converted_terms.terms_higher_than_or_equal_to(
self._ranking_cutoff)
logging.debug("Cut terms: %r", cut_terms)
try:
medline_record_mesh_terms=ExpressionList().from_medline(
each_article.set_id.article_record()['MH'])
except:
logging.warn("Could not obtain an article record for %r. "
"Skipping.", each_article)
return
flat_medline=medline_record_mesh_terms.flatten()
flattened_terms=self.flatten_generated_terms(flat_medline,
cut_terms)
flattened_terms=self.limit_length(flat_medline, flattened_terms)
if len(flat_medline)==0:
logging.warn("No gold standard available for article %r. "
"Omitting it from the result set.", each_article)
return
eval_result=self.perform_evaluation(each_article,
self.evaluator,
flat_medline,
flattened_terms)
flattened_major_headings=\
medline_record_mesh_terms.major_headings()
#logging.debug("Original headings: %r Major headings: %r",
# medline_record_mesh_terms,
# flattened_major_headings)
logging.debug("Flattened MeSH terms: %r", flat_medline)
logging.debug("Flattened generated terms: %r", flattened_terms)
mh_result_temp=self.perform_evaluation(each_article, self.evaluator,
flattened_major_headings,
flattened_terms)
mh_result=NamedResultSet("major_", mh_result_temp)
# Compute the total recall, too
total_recall=self.compute_total_recall(flat_medline,
converted_terms)
eval_result.add(total_recall)
# Unify the result sets
self.all_results[each_article.set_id]=eval_result | mh_result
return
def process_article(self, each_article):
if not self.include_article(each_article):
logging.log(ULTRADEBUG, "Skipping article %r due to exclusion "
" criteria.", each_article)
return
try:
ranked_article = self.graph_and_rank(each_article)
except CouldNotRank:
return
logging.debug("Ranked article: %r", ranked_article)
converted_terms = self.convert(ranked_article)
logging.debug("Converted terms: %r", converted_terms)
cut_terms = converted_terms.terms_higher_than_or_equal_to(
self._ranking_cutoff)
logging.debug("Cut terms: %r", cut_terms)
try:
medline_record_mesh_terms = ExpressionList().from_medline(
each_article.set_id.article_record()['MH'])
except:
logging.warn(
"Could not obtain an article record for %r. "
"Skipping.", each_article)
return
flat_medline = medline_record_mesh_terms.flatten()
flattened_terms = self.flatten_generated_terms(flat_medline, cut_terms)
flattened_terms = self.limit_length(flat_medline, flattened_terms)
if len(flat_medline) == 0:
logging.warn(
"No gold standard available for article %r. "
"Omitting it from the result set.", each_article)
return
eval_result = self.perform_evaluation(each_article, self.evaluator,
flat_medline, flattened_terms)
flattened_major_headings=\
medline_record_mesh_terms.major_headings()
#logging.debug("Original headings: %r Major headings: %r",
# medline_record_mesh_terms,
# flattened_major_headings)
logging.debug("Flattened MeSH terms: %r", flat_medline)
logging.debug("Flattened generated terms: %r", flattened_terms)
mh_result_temp = self.perform_evaluation(each_article, self.evaluator,
flattened_major_headings,
flattened_terms)
mh_result = NamedResultSet("major_", mh_result_temp)
# Compute the total recall, too
total_recall = self.compute_total_recall(flat_medline, converted_terms)
eval_result.add(total_recall)
# Unify the result sets
self.all_results[each_article.set_id] = eval_result | mh_result
return
def as_ExpressionList(self):
"Returns an ExpressionList containing the results of the conversion"
return ExpressionList([x[0] for x in self])
