def get_summary(topic_path,
summary_size=100,
oracle="accept_reject",
summarizer="sume",
parser=None,
language="english",
rouge_dir="rouge/RELEASE-1.5.5/"):
# relativize the topic path!!!!
if topic_path.startswith("/"):
relative_path = re.search('^(/)(.*)$', topic_path).group(2)
else:
relative_path = topic_path
resolved_topic_path = path.normpath(
path.join(path.expanduser("~"), ".ukpsummarizer",
path.normpath(relative_path)))
topic = Topic(resolved_topic_path)
docs = topic.get_docs()
models = topic.get_models()
if summarizer == "sume":
sw = SumeWrap(language)
summary = sw(docs, summary_size)
return summary
elif summarizer == "custom_weights":
sw = SumeWrap(language)
return "no summary for summarizer type %s" % summarizer
class Summary(object):
def __init__(self, summary_file):
p, f = path.split(summary_file)
self.topic = Topic(path.normpath(path.join(p, "..")))
self.idx = None
for i, (fn, t) in enumerate(self.topic.get_models()):
if fn.startswith(summary_file):
self.idx = i
print(f, self.idx)
def get_index(self):
return self.idx
def get_topic(self):
return self.topic
def run(self,
topic_path,
size=None,
summarizer="SUME",
summary_idx=None,
parser=None,
oracle="accept",
feedback_log=None,
propagation=False,
max_iteration_count=10,
preload_embeddings=None,
feedbackstore=None,
override_results_files=False,
num_clusters=8):
log = logging.getLogger("SingleTopicRunner")
sf = None # just for the sake of being able to run without simulated feedback...
self.tlog.debug("SingleTopicRunner started")
# relativize the topic path!
if type(topic_path) is Topic:
topic = topic_path
else:
if topic_path.startswith("/"):
relative_path = re.search('^(/)(.*)$', topic_path).group(2)
else:
relative_path = topic_path
topic = Topic(
path.join(self.iobasedir, path.normpath(relative_path)))
language = topic.get_language()
docs = topic.get_docs()
summaries = topic.get_models()
flightrecorder = get_flightrecorder_from_file(feedback_log)
preceding_size = len(
flightrecorder.records
) # the number of iterations that happened due to the provided feedback_log
embeddings = None
"""
if preload_embeddings:
embeddings_path = path.normpath(path.join(self.iobasedir, "embeddings"))
embeddings = load_w2v_embeddings(embeddings_path, language, 'active_learning')
else:
embeddings = preload_embeddings
"""
if summary_idx is not None:
summaries = [summaries[summary_idx]]
if size is None:
use_size = topic.get_summary_size()
else:
use_size = size
clusters_path = path.join(self.iobasedir, 'clustering',
'{}'.format(num_clusters))
#print(clusters_path)
#clusters = get_clusters(clusters_path, topic.docs_dir)
if summarizer == "SUME":
sw = SumeWrap(language)
summary = sw(docs, use_size)
outputfilecontents = {
"summary": summary,
"type": summarizer,
"info_data": []
}
json_content = json.dumps(outputfilecontents)
if self.out is not None:
log.info("writing output to %s" % (self.out))
write_to_file(json_content, self.out)
write_to_file(
json_content,
path.normpath(
path.expanduser(
path.join(self.iobasedir, "tmp", "tmp.json"))))
elif summarizer == "UPPER_BOUND":
ub_summary = load_ub_summary(language,
docs,
summaries,
use_size,
base_dir=self.iobasedir)
summary = '\n'.join(ub_summary)
outputfilecontents = {
"summary": summary,
"type": summarizer,
"info_data": []
}
json_content = json.dumps(outputfilecontents)
if self.out is not None:
log.info("writing output to %s" % (self.out))
write_to_file(json_content, self.out)
write_to_file(
json_content,
path.normpath(
path.expanduser(
path.join(self.iobasedir, "tmp", "tmp.json"))))
elif summarizer == "PROPAGATION":
#UB considering all the summaries
ub_summary = load_ub_summary(language,
docs,
summaries,
use_size,
base_dir=self.iobasedir)
summary = '\n'.join(ub_summary)
ub_scores = self.rouge(summary, summaries, use_size)
log.debug(
"UB scores: R1:%s R2:%s SU4:%s" %
(str(ub_scores[0]), str(ub_scores[1]), str(ub_scores[2])))
ref_summ = random.choice(summaries)
parse_info = []
#parse_info = topic.get_parse_info(summaries.index(ref_summ))
# initialize the Algorithm.
run_config = dict()
run_config['rank_subset'] = True
run_config['relative_k'] = True
run_config['dynamic_k'] = False
for flag in ['adaptive_sampling', 'strategy']:
run_config[flag] = False
r = 0
clusters = None
log.info("recording k_size in summarize %f", self.k)
#TODO: Added summaries instead of one single summary
sf = SimulatedFeedback(
language,
self.rouge,
embeddings=None, #TODO: embeddings
docs=docs,
models=summaries,
summary_length=use_size,
oracle_type=oracle,
ub_score=ub_scores,
ub_summary=ub_summary,
parser_type=parser,
flightrecorder=flightrecorder,
feedbackstore=feedbackstore,
parse_info=parse_info,
run_config=run_config,
k=self.k,
adaptive_window_size=r,
clusters=clusters)
if sf.embeddings is None or sf.embeddings == {}:
embe_var = "none",
else:
if sf.embeddings.embedding_variant is None:
embe_var = "none"
else:
embe_var = sf.embeddings.embedding_variant
if feedbackstore is None:
cfg = {"type": "Unconfigured default"}
else:
cfg = feedbackstore.get_config()
rs = []
for p, t in [ref_summ]:
rs.append({"name": os.path.split(p)[1], "text": t})
run_id_string = "%s-%s-%s-%s-%s-%s-%s-%s" % (
oracle, summarizer, parser, embe_var, topic.get_dataset(),
topic.get_name(), [item["name"]
for item in rs], json.dumps(cfg))
run_id = hashlib.sha224(run_id_string).hexdigest()
filename = path.join(self.scores_storage_path,
"result-%s.json" % (run_id))
if (os.path.exists(filename) and self.out is None
and self.override_results_switch is False):
log.info(
"Skipping run_id '%s' because the result file does already exist. config: %s"
% (run_id, run_id_string))
return
else:
log.info("Doing %s iterations for run_id '%s'\n %s" %
(max_iteration_count, run_id, run_id_string))
write_to_file("", filename)
summary, confirmatory_summary, exploratory_summary = sf.run_full_simulation(
max_iteration_count=max_iteration_count)
recommendations, recom_sentences = sf.get_recommendations()
derived_records = []
# construct table-like array of feedbacks per iteration.
for i, record in enumerate(sf.flight_recorder.records):
for accept in record.accept:
derived_records.append({
"iteration": i,
"concept": accept,
"value": "accept"
})
for reject in record.reject:
derived_records.append({
"iteration": i,
"concept": reject,
"value": "reject"
})
for implicit_reject in record.implicit_reject:
derived_records.append({
"iteration": i,
"concept": implicit_reject,
"value": "implicit_reject"
})
for item in recommendations:
derived_records.append({
"iteration":
-1,
"concept":
item,
"value":
"recommendation",
"weight":
sf.summarizer.weights.get(item, 0.0),
"uncertainity":
sf.svm_uncertainity.get(item, -1.0)
})
result = {
"config_run_id": run_id,
"config_oracle_type": oracle,
"config_summarizer_type": summarizer,
"config_parse_type": str(parser),
#"config_wordembeddings": emb_var,
"config_feedbackstore": sf.feedbackstore.get_config(),
"config_feedback_interpretation": {},
"config_concept_recommendation": {},
"dataset": topic.get_dataset(),
"topic": topic.get_name(),
"models": rs,
"model_rougescores": {
"iteration": -1,
"ROUGE-1 R score": ub_scores[0],
"ROUGE-2 R score": ub_scores[1],
"ROUGE-SU* R score": ub_scores[2],
"accepted": [],
"accept_count": 0,
"rejected": [],
"reject_count": 0,
"summary": ub_summary
},
"result_summary": summary,
"result_rougescores": sf.log_sir_info_data,
"log_feedbacks": derived_records
}
r2 = [{
"iteration": i,
"summary": sf.log_info_data[i]
} for i in range(len(sf.flight_recorder.records))]
log.debug(
"records: %s, infos %s, diff: %s" %
(len(sf.flight_recorder.records), len(sf.log_info_data),
len(sf.flight_recorder.records) - len(sf.log_info_data)))
write_to_file(json.dumps(result), filename)
log.info("Writing results to %s" % (filename))
df = pd.DataFrame(derived_records)
filename = path.join(self.scores_storage_path,
"flightrecorder-%s.csv" % (run_id))
log.info("saving flightrecorder to %s with run_id %s" %
(filename, run_id))
df.to_csv(filename, encoding="UTF-8")
write_to_file(
json.dumps(sf.new_debug_weights_history),
path.join(
self.scores_storage_path,
"weightshistory-%s-%s-%s-%s.json" %
(topic.get_dataset(), topic.get_name(), summarizer,
run_id)))
log.info("Writing weights history to %s" % (filename))
weights_hist = pd.DataFrame(sf.new_debug_weights_history)
filename = path.join(self.scores_storage_path,
"weightshistory-%s.csv" % (run_id))
weights_hist.to_csv(filename, encoding="UTF-8")
log.debug("----------------------------------------------")
log.debug(summary)
log.debug(sf.log_info_data[-1])
log.debug("----------------------------------------------")
if self.pickle_store is not None:
# Pickle dictionary using protocol 0.
print('Pickle in file %s' % self.pickle_store)
self.pickle_write(sf, self.pickle_store, log)
json_content = self.write_summarize_output_json(
sf, confirmatory_summary, derived_records, log,
recom_sentences, result, run_id, summarizer, summary,
self.pickle_store)
# write_to_file(json_content, path.normpath(path.expanduser(path.join(self.iobasedir, "tmp", "tmp.json"))))
else:
raise BaseException("You should tell which summarizer to use")
if sf is not None:
write_details_file([sf.log_info_data],
path.join(self.iobasedir, "tmp", "tmp.csv"))
self.tlog.debug("SingleTopicRunner finished")
d = DataSet(f)
# unroll to get topics
for t in d.get_topics():
for (mf, mt) in t.get_models():
mf = path.normpath(mf)
pref = path.commonprefix([mf, iobasedir])
tn = mf[len(pref) + 1:]
print("shortened:", tn)
queue.append(mf)
# topics.append([t.get_name for t in d.get_topics()])
elif path.exists(path.join(f, "task.json")):
# is topic
t = Topic(f)
for (mf, mt) in t.get_models():
mf = path.normpath(mf)
pref = path.commonprefix([mf, iobasedir])
tn = mf[len(pref) + 1:]
print("shortened:", tn)
queue.append(mf)
elif path.exists(path.join(f, "..", "..", "task.json")) \
and path.exists(f):
# should be model
queue.append(f)
else:
raise BaseException("Invalid file given.", f, " is neither a dataset nor a topic nor a model.")
if args.max_models:
queue = queue[:args.max_models]
