def make_fa_summaries(self,
eval_data,
prefix,
feature_set,
apsal_sal=.4,
apsal_sim=.7,
**kwargs):
apsal = APSalienceSummarizer()
jobs = []
for event, corpus in eval_data:
print event.fs_name()
apsal_tsv_dir = apsal.get_tsv_dir(prefix, feature_set)
if not os.path.exists(apsal_tsv_dir):
os.makedirs(apsal_tsv_dir)
jobs.append((event, corpus, prefix, feature_set, apsal))
self.do_work(cluster_worker, jobs, **kwargs)
print "Generating AP+Salience Summary"
print "\tSal Threshold: {}".format(apsal_sal)
print "\tSim Threshold: {}".format(apsal_sim)
jobs = []
for event, corpus in eval_data:
jobs.append((APSalienceFilteredSummary(),
(event, prefix, feature_set, apsal_sal, apsal_sim)))
self.do_work(filter_worker, jobs, **kwargs)
def make_fa_summaries(self, eval_data, prefix, feature_set,
apsal_sal=.4, apsal_sim=.7, **kwargs):
apsal = APSalienceSummarizer()
jobs = []
for event, corpus in eval_data:
print event.fs_name()
apsal_tsv_dir = apsal.get_tsv_dir(prefix, feature_set)
if not os.path.exists(apsal_tsv_dir):
os.makedirs(apsal_tsv_dir)
jobs.append((event, corpus, prefix, feature_set, apsal))
self.do_work(cluster_worker, jobs, **kwargs)
print "Generating AP+Salience Summary"
print "\tSal Threshold: {}".format(apsal_sal)
print "\tSim Threshold: {}".format(apsal_sim)
jobs = []
for event, corpus in eval_data:
jobs.append(
(APSalienceFilteredSummary(),
(event, prefix, feature_set, apsal_sal,
apsal_sim)))
self.do_work(filter_worker, jobs, **kwargs)
def filter_worker(job_queue, result_queue, **kwargs):
signal.signal(signal.SIGINT, signal.SIG_IGN)
import time
time.sleep(1)
while not job_queue.empty():
try:
summarizer, sum_args = job_queue.get(block=False)
if isinstance(summarizer, APFilteredSummary):
event, sim_cutoff = sum_args
ap = APSummarizer()
ap_tsv_path = ap.get_tsv_path(event)
apf_tsv_path = summarizer.get_tsv_path(event, sim_cutoff)
if os.path.exists(ap_tsv_path) and \
not os.path.exists(apf_tsv_path):
summarizer.make(event, sim_threshold=sim_cutoff)
elif isinstance(summarizer, HACFilteredSummary):
event, prefix, feature_set, dist_cutoff, sim_cutoff = sum_args
hac = HACSummarizer()
hac_tsv_path = hac.get_tsv_path(event, dist_cutoff)
hacf_tsv_path = summarizer.get_tsv_path(
event, dist_cutoff, sim_cutoff)
if os.path.exists(hac_tsv_path) and \
not os.path.exists(hacf_tsv_path):
try:
summarizer.make(event,
prefix,
feature_set,
dist_cutoff=dist_cutoff,
sim_threshold=sim_cutoff)
except Exception, e:
print e
print hac_tsv_path
elif isinstance(summarizer, APSalienceFilteredSummary) or \
isinstance(summarizer, APSalTRankSalThreshFilteredSummary):
event, prefix, feature_set, sal_cutoff, sim_cutoff = sum_args
aps = APSalienceSummarizer()
aps_tsv_path = aps.get_tsv_path(event, prefix, feature_set)
apsf_tsv_path = summarizer.get_tsv_path(
event, prefix, feature_set, sal_cutoff, sim_cutoff)
if os.path.exists(aps_tsv_path) and \
not os.path.exists(apsf_tsv_path):
summarizer.make(event,
prefix,
feature_set,
min_cluster_size=2,
center_threshold=sal_cutoff,
sim_threshold=sim_cutoff)
elif isinstance(summarizer, RankedSalienceFilteredSummary):
event, prefix, feature_set, sal_cutoff, sim_cutoff = sum_args
rsfs_tsv_path = summarizer.get_tsv_path(
event, sal_cutoff, sim_cutoff)
if not os.path.exists(rsfs_tsv_path):
summarizer.make(event, prefix, feature_set, sal_cutoff,
sim_cutoff)
def filter_worker(job_queue, result_queue, **kwargs):
signal.signal(signal.SIGINT, signal.SIG_IGN)
import time
time.sleep(1)
while not job_queue.empty():
try:
summarizer, sum_args = job_queue.get(block=False)
if isinstance(summarizer, APFilteredSummary):
event, sim_cutoff = sum_args
ap = APSummarizer()
ap_tsv_path = ap.get_tsv_path(event)
apf_tsv_path = summarizer.get_tsv_path(event, sim_cutoff)
if os.path.exists(ap_tsv_path) and \
not os.path.exists(apf_tsv_path):
summarizer.make(event, sim_threshold=sim_cutoff)
elif isinstance(summarizer, HACFilteredSummary):
event, prefix, feature_set, dist_cutoff, sim_cutoff = sum_args
hac = HACSummarizer()
hac_tsv_path = hac.get_tsv_path(event, dist_cutoff)
hacf_tsv_path = summarizer.get_tsv_path(
event, dist_cutoff, sim_cutoff)
if os.path.exists(hac_tsv_path) and \
not os.path.exists(hacf_tsv_path):
try:
summarizer.make(event, prefix, feature_set,
dist_cutoff=dist_cutoff,
sim_threshold=sim_cutoff)
except Exception, e:
print e
print hac_tsv_path
elif isinstance(summarizer, APSalienceFilteredSummary) or \
isinstance(summarizer, APSalTRankSalThreshFilteredSummary):
event, prefix, feature_set, sal_cutoff, sim_cutoff = sum_args
aps = APSalienceSummarizer()
aps_tsv_path = aps.get_tsv_path(event, prefix, feature_set)
apsf_tsv_path = summarizer.get_tsv_path(
event, prefix, feature_set, sal_cutoff, sim_cutoff)
if os.path.exists(aps_tsv_path) and \
not os.path.exists(apsf_tsv_path):
summarizer.make(
event, prefix, feature_set,
min_cluster_size=2, center_threshold=sal_cutoff,
sim_threshold=sim_cutoff)
elif isinstance(summarizer, RankedSalienceFilteredSummary):
event, prefix, feature_set, sal_cutoff, sim_cutoff = sum_args
rsfs_tsv_path = summarizer.get_tsv_path(
event, sal_cutoff, sim_cutoff)
if not os.path.exists(rsfs_tsv_path):
summarizer.make(
event, prefix, feature_set,
sal_cutoff, sim_cutoff)
def tune_fa(self,
dev_data,
prefix,
feature_set,
sal_min=-2.0,
sal_max=2.0,
sal_step=.1,
sem_sim_min=.2,
sem_sim_max=.7,
sem_sim_step=.05,
**kwargs):
apsal = APSalienceSummarizer()
sal_cutoffs = np.arange(sal_min, sal_max + sal_step, sal_step)
sem_sim_cutoffs = np.arange(sem_sim_min, sem_sim_max + sem_sim_step,
sem_sim_step)
print "Tuning on dev data."
### Run clustering ###
print "Generating AP+Salience Cluster\n\t(no params)"
jobs = []
for event, corpus in dev_data:
print event.fs_name()
apsal_tsv_dir = apsal.get_tsv_dir(prefix, feature_set)
if not os.path.exists(apsal_tsv_dir):
os.makedirs(apsal_tsv_dir)
jobs.append((event, corpus, prefix, feature_set, apsal))
self.do_work(cluster_worker, jobs, **kwargs)
### Run filtering ###
print
print "Generating AP+Salience Summary"
print "\tSal Threshold ({}, {}), step={}".format(
sal_min, sal_max, sal_step)
print "\tSim Threshold ({}, {}), step={}".format(
sem_sim_min, sem_sim_max, sem_sim_step)
print "\t{} jobs/event".format(sal_cutoffs.shape[0] *
sem_sim_cutoffs.shape[0])
jobs = []
for event, corpus in dev_data:
for sem_sim_cutoff in sem_sim_cutoffs:
for sal_cutoff in sal_cutoffs:
jobs.append((APSalienceFilteredSummary(),
(event, prefix, feature_set, sal_cutoff,
sem_sim_cutoff)))
self.do_work(filter_worker, jobs, **kwargs)
def tune_fa(self, dev_data, prefix, feature_set,
sal_min=-2.0, sal_max=2.0, sal_step=.1,
sem_sim_min=.2, sem_sim_max=.7, sem_sim_step=.05,
**kwargs):
apsal = APSalienceSummarizer()
sal_cutoffs = np.arange(
sal_min, sal_max + sal_step, sal_step)
sem_sim_cutoffs = np.arange(
sem_sim_min, sem_sim_max + sem_sim_step, sem_sim_step)
print "Tuning on dev data."
### Run clustering ###
print "Generating AP+Salience Cluster\n\t(no params)"
jobs = []
for event, corpus in dev_data:
print event.fs_name()
apsal_tsv_dir = apsal.get_tsv_dir(prefix, feature_set)
if not os.path.exists(apsal_tsv_dir):
os.makedirs(apsal_tsv_dir)
jobs.append((event, corpus, prefix, feature_set, apsal))
self.do_work(cluster_worker, jobs, **kwargs)
### Run filtering ###
print
print "Generating AP+Salience Summary"
print "\tSal Threshold ({}, {}), step={}".format(
sal_min, sal_max, sal_step)
print "\tSim Threshold ({}, {}), step={}".format(
sem_sim_min, sem_sim_max, sem_sim_step)
print "\t{} jobs/event".format(
sal_cutoffs.shape[0] * sem_sim_cutoffs.shape[0])
jobs = []
for event, corpus in dev_data:
for sem_sim_cutoff in sem_sim_cutoffs:
for sal_cutoff in sal_cutoffs:
jobs.append(
(APSalienceFilteredSummary(),
(event, prefix, feature_set, sal_cutoff,
sem_sim_cutoff)))
self.do_work(filter_worker, jobs, **kwargs)
def make_summaries(self,
eval_data,
prefix,
feature_set,
hac_dist=1.35,
hac_sim=.7,
ap_sim=.7,
apsal_sal=.4,
apsal_sim=.7,
apsal_tr_sal=.6,
apsal_tr_sim=.6,
sal_rank_sal=1.8,
sal_rank_sim=.4,
**kwargs):
ap = APSummarizer()
apsal = APSalienceSummarizer()
hac = HACSummarizer()
print "Running with optimal params on dev data."
### Run clustering ###
print "Generating AP Cluster\n\t(no params)"
print "Generating AP+Salience Cluster\n\t(no params)"
print "Generating HAC Cluster\n\tdist-thresh: {}".format(hac_dist)
jobs = []
for event, corpus in eval_data:
print event.fs_name()
apsal_tsv_dir = apsal.get_tsv_dir(prefix, feature_set)
if not os.path.exists(apsal_tsv_dir):
os.makedirs(apsal_tsv_dir)
if not os.path.exists(ap.dir_):
os.makedirs(ap.dir_)
if not os.path.exists(hac.dir_):
os.makedirs(hac.dir_)
jobs.append((event, corpus, prefix, feature_set, hac, hac_dist))
jobs.append((event, corpus, prefix, feature_set, ap))
jobs.append((event, corpus, prefix, feature_set, apsal))
self.do_work(cluster_worker, jobs, **kwargs)
### Run filtering ###
print
print "Generating AP Summary"
print "\tSim Threshold: {}".format(ap_sim)
print "Generating AP+Salience Summary"
print "\tSal Threshold: {}".format(apsal_sal)
print "\tSim Threshold: {}".format(apsal_sim)
print "Generating HAC Summary"
print "\tDist Threshold: {}".format(hac_dist)
print "\tSim Threshold: {}".format(hac_sim)
print "Generating AP+Salience Time Ranked"
print "\tSal Threshold: {}".format(apsal_tr_sal)
print "\tSim Threshold: {}".format(apsal_tr_sim)
print "Generating Salience Ranked Summary"
print "\tSal Threshold: {}".format(sal_rank_sal)
print "\tSim Threshold: {}".format(sal_rank_sim)
jobs = []
for event, corpus in eval_data:
jobs.append((HACFilteredSummary(), (event, prefix, feature_set,
hac_dist, hac_sim)))
jobs.append((APFilteredSummary(), (event, ap_sim)))
jobs.append((APSalienceFilteredSummary(),
(event, prefix, feature_set, apsal_sal, apsal_sim)))
jobs.append(
(APSalTRankSalThreshFilteredSummary(),
(event, prefix, feature_set, apsal_tr_sal, apsal_tr_sim)))
jobs.append(
(RankedSalienceFilteredSummary(),
(event, prefix, feature_set, sal_rank_sal, sal_rank_sim)))
self.do_work(filter_worker, jobs, **kwargs)
def tune(self,
dev_data,
prefix,
feature_set,
hac_dist_min=.9,
hac_dist_max=5.05,
hac_dist_step=.05,
sal_min=-2.0,
sal_max=2.0,
sal_step=.1,
sem_sim_min=.2,
sem_sim_max=.7,
sem_sim_step=.05,
rank_sim_min=.2,
rank_sim_max=.4,
rank_sim_step=.05,
**kwargs):
ap = APSummarizer()
apsal = APSalienceSummarizer()
hac = HACSummarizer()
hac_dist_cutoffs = np.arange(hac_dist_min,
hac_dist_max + hac_dist_step,
hac_dist_step)
sal_cutoffs = np.arange(sal_min, sal_max + sal_step, sal_step)
sem_sim_cutoffs = np.arange(sem_sim_min, sem_sim_max + sem_sim_step,
sem_sim_step)
rank_sim_cutoffs = np.arange(rank_sim_min,
rank_sim_max + rank_sim_step,
rank_sim_step)
print "Tuning on dev data."
### Run clustering ###
print "Generating AP Cluster\n\t(no params)"
print "Generating AP+Salience Cluster\n\t(no params)"
print "Generating HAC Cluster"
print "\tDist Threshold ({}, {}), step={} {} jobs/event".format(
hac_dist_min, hac_dist_max, hac_dist_step,
hac_dist_cutoffs.shape[0])
jobs = []
for event, corpus in dev_data:
print event.fs_name()
apsal_tsv_dir = apsal.get_tsv_dir(prefix, feature_set)
if not os.path.exists(apsal_tsv_dir):
os.makedirs(apsal_tsv_dir)
if not os.path.exists(ap.dir_):
os.makedirs(ap.dir_)
if not os.path.exists(hac.dir_):
os.makedirs(hac.dir_)
for cutoff in hac_dist_cutoffs:
jobs.append((event, corpus, prefix, feature_set, hac, cutoff))
jobs.append((event, corpus, prefix, feature_set, ap))
jobs.append((event, corpus, prefix, feature_set, apsal))
self.do_work(cluster_worker, jobs, **kwargs)
### Run filtering ###
print
print "Generating AP Summary"
print "\tSim Threshold ({}, {}), step={}".format(
sem_sim_min, sem_sim_max, sem_sim_step)
print "\t{} jobs/event".format(sem_sim_cutoffs.shape[0])
print "Generating AP+Salience Summary"
print "\tSal Threshold ({}, {}), step={}".format(
sal_min, sal_max, sal_step)
print "\tSim Threshold ({}, {}), step={}".format(
sem_sim_min, sem_sim_max, sem_sim_step)
print "\t{} jobs/event".format(sal_cutoffs.shape[0] *
sem_sim_cutoffs.shape[0])
print "Generating HAC Summary"
print "\tDist Threshold ({}, {}), step={}".format(
hac_dist_min, hac_dist_max, hac_dist_step)
print "\tSim Threshold ({}, {}), step={}".format(
sem_sim_min, sem_sim_max, sem_sim_step)
print "\t{} jobs/event".format(hac_dist_cutoffs.shape[0] *
sem_sim_cutoffs.shape[0])
rsfs = RankedSalienceFilteredSummary()
if not os.path.exists(rsfs.dir_):
os.makedirs(rsfs.dir_)
jobs = []
for event, corpus in dev_data:
for sem_sim_cutoff in sem_sim_cutoffs:
for dist_cutoff in hac_dist_cutoffs:
jobs.append(
(HACFilteredSummary(), (event, prefix, feature_set,
dist_cutoff, sem_sim_cutoff)))
jobs.append((APFilteredSummary(), (event, sem_sim_cutoff)))
for sal_cutoff in sal_cutoffs:
jobs.append((APSalienceFilteredSummary(),
(event, prefix, feature_set, sal_cutoff,
sem_sim_cutoff)))
jobs.append((APSalTRankSalThreshFilteredSummary(),
(event, prefix, feature_set, sal_cutoff,
sem_sim_cutoff)))
for rank_sim_cutoff in rank_sim_cutoffs:
for sal_cutoff in sal_cutoffs:
jobs.append((RankedSalienceFilteredSummary(),
(event, prefix, feature_set, sal_cutoff,
rank_sim_cutoff)))
self.do_work(filter_worker, jobs, **kwargs)
def make(self,
event,
prefix,
feature_set,
min_cluster_size=2,
sim_threshold=.2264,
center_threshold=1.0):
tsv_path = self.get_tsv_path(event, prefix, feature_set,
center_threshold, sim_threshold)
lvecs = SentenceLatentVectorsResource()
spa = SaliencePredictionAggregator()
apsal = APSalienceSummarizer()
cluster_df = apsal.get_dataframe(event, prefix, feature_set)
#for _, row in cluster_df.iterrows():
# print row['hour'], datetime.utcfromtimestamp(row['timestamp'])
updates = []
Xcache = None
timestamps = sorted(list(cluster_df['timestamp'].unique()))
for timestamp in timestamps:
hour = datetime.utcfromtimestamp(timestamp) - timedelta(hours=1)
lvec_df = lvecs.get_dataframe(event, hour)
lvec_df.drop_duplicates(['stream id', 'sentence id'], inplace=True)
sal_df = spa.get_dataframe(event, hour, prefix, feature_set)
sal_df.drop_duplicates(['stream id', 'sentence id'], inplace=True)
clusters = cluster_df[cluster_df['timestamp'] == timestamp].copy()
clusters.sort(['stream id', 'sentence id'], inplace=True)
salience = []
for _, row in clusters.iterrows():
sal_pred = sal_df.loc[
(sal_df['stream id'] == row['stream id']) & \
(sal_df['sentence id'] == row['sentence id'])].as_matrix()[:,2:].astype(np.float64).mean()
salience.append(sal_pred)
clusters['salience'] = salience
#clusters.sort(['salie'], inplace=True)
sal_mean = np.mean(salience)
sal_std = np.std(salience)
#print clusters
for _, row in clusters.iterrows():
if row['cluster size'] < min_cluster_size:
continue
vec = lvec_df.loc[
(lvec_df['stream id'] == row['stream id']) & \
(lvec_df['sentence id'] == row['sentence id'])].as_matrix()[:,2:].astype(np.float64)
sal_norm = (row['salience'] - sal_mean) / sal_std
if sal_norm < center_threshold:
continue
if Xcache is None:
Xcache = vec
else:
if np.max(cosine_similarity(Xcache, vec)) >= sim_threshold:
continue
else:
Xcache = np.vstack((Xcache, vec))
updates.append({
'query id':
event.query_id[5:],
'system id':
'cunlp',
'run id':
'apsal-time-ranked-sal_{}-sim_{}'.format(
center_threshold, sim_threshold),
'stream id':
row['stream id'],
'sentence id':
row['sentence id'],
'timestamp':
timestamp,
'conf':
row['salience'],
'string':
row['string']
})
print "Writing", tsv_path, "For ", center_threshold, sim_threshold
df = pd.DataFrame(updates,
columns=[
"query id", "system id", "run id", "stream id",
"sentence id", "timestamp", "conf", "string"
])
with open(tsv_path, u'w') as f:
df.to_csv(f,
sep='\t',
index=False,
index_label=False,
header=False)
def make_summaries(self, eval_data, prefix, feature_set,
hac_dist=1.35, hac_sim=.7, ap_sim=.7,
apsal_sal=.4, apsal_sim=.7,
apsal_tr_sal=.6, apsal_tr_sim=.6,
sal_rank_sal=1.8, sal_rank_sim=.4, **kwargs):
ap = APSummarizer()
apsal = APSalienceSummarizer()
hac = HACSummarizer()
print "Running with optimal params on dev data."
### Run clustering ###
print "Generating AP Cluster\n\t(no params)"
print "Generating AP+Salience Cluster\n\t(no params)"
print "Generating HAC Cluster\n\tdist-thresh: {}".format(hac_dist)
jobs = []
for event, corpus in eval_data:
print event.fs_name()
apsal_tsv_dir = apsal.get_tsv_dir(prefix, feature_set)
if not os.path.exists(apsal_tsv_dir):
os.makedirs(apsal_tsv_dir)
if not os.path.exists(ap.dir_):
os.makedirs(ap.dir_)
if not os.path.exists(hac.dir_):
os.makedirs(hac.dir_)
jobs.append(
(event, corpus, prefix, feature_set, hac, hac_dist))
jobs.append((event, corpus, prefix, feature_set, ap))
jobs.append((event, corpus, prefix, feature_set, apsal))
self.do_work(cluster_worker, jobs, **kwargs)
### Run filtering ###
print
print "Generating AP Summary"
print "\tSim Threshold: {}".format(ap_sim)
print "Generating AP+Salience Summary"
print "\tSal Threshold: {}".format(apsal_sal)
print "\tSim Threshold: {}".format(apsal_sim)
print "Generating HAC Summary"
print "\tDist Threshold: {}".format(hac_dist)
print "\tSim Threshold: {}".format(hac_sim)
print "Generating AP+Salience Time Ranked"
print "\tSal Threshold: {}".format(apsal_tr_sal)
print "\tSim Threshold: {}".format(apsal_tr_sim)
print "Generating Salience Ranked Summary"
print "\tSal Threshold: {}".format(sal_rank_sal)
print "\tSim Threshold: {}".format(sal_rank_sim)
jobs = []
for event, corpus in eval_data:
jobs.append(
(HACFilteredSummary(),
(event, prefix, feature_set,
hac_dist, hac_sim)))
jobs.append(
(APFilteredSummary(), (event, ap_sim)))
jobs.append(
(APSalienceFilteredSummary(),
(event, prefix, feature_set, apsal_sal,
apsal_sim)))
jobs.append(
(APSalTRankSalThreshFilteredSummary(),
(event, prefix, feature_set, apsal_tr_sal,
apsal_tr_sim)))
jobs.append(
(RankedSalienceFilteredSummary(),
(event, prefix, feature_set, sal_rank_sal,
sal_rank_sim)))
self.do_work(filter_worker, jobs, **kwargs)
def tune(self, dev_data, prefix, feature_set,
hac_dist_min=.9, hac_dist_max=5.05, hac_dist_step=.05,
sal_min=-2.0, sal_max=2.0, sal_step=.1,
sem_sim_min=.2, sem_sim_max=.7, sem_sim_step=.05,
rank_sim_min=.2, rank_sim_max=.4, rank_sim_step=.05, **kwargs):
ap = APSummarizer()
apsal = APSalienceSummarizer()
hac = HACSummarizer()
hac_dist_cutoffs = np.arange(
hac_dist_min, hac_dist_max + hac_dist_step, hac_dist_step)
sal_cutoffs = np.arange(
sal_min, sal_max + sal_step, sal_step)
sem_sim_cutoffs = np.arange(
sem_sim_min, sem_sim_max + sem_sim_step, sem_sim_step)
rank_sim_cutoffs = np.arange(
rank_sim_min, rank_sim_max + rank_sim_step, rank_sim_step)
print "Tuning on dev data."
### Run clustering ###
print "Generating AP Cluster\n\t(no params)"
print "Generating AP+Salience Cluster\n\t(no params)"
print "Generating HAC Cluster"
print "\tDist Threshold ({}, {}), step={} {} jobs/event".format(
hac_dist_min, hac_dist_max, hac_dist_step,
hac_dist_cutoffs.shape[0])
jobs = []
for event, corpus in dev_data:
print event.fs_name()
apsal_tsv_dir = apsal.get_tsv_dir(prefix, feature_set)
if not os.path.exists(apsal_tsv_dir):
os.makedirs(apsal_tsv_dir)
if not os.path.exists(ap.dir_):
os.makedirs(ap.dir_)
if not os.path.exists(hac.dir_):
os.makedirs(hac.dir_)
for cutoff in hac_dist_cutoffs:
jobs.append(
(event, corpus, prefix, feature_set, hac, cutoff))
jobs.append((event, corpus, prefix, feature_set, ap))
jobs.append((event, corpus, prefix, feature_set, apsal))
self.do_work(cluster_worker, jobs, **kwargs)
### Run filtering ###
print
print "Generating AP Summary"
print "\tSim Threshold ({}, {}), step={}".format(
sem_sim_min, sem_sim_max, sem_sim_step)
print "\t{} jobs/event".format(
sem_sim_cutoffs.shape[0])
print "Generating AP+Salience Summary"
print "\tSal Threshold ({}, {}), step={}".format(
sal_min, sal_max, sal_step)
print "\tSim Threshold ({}, {}), step={}".format(
sem_sim_min, sem_sim_max, sem_sim_step)
print "\t{} jobs/event".format(
sal_cutoffs.shape[0] * sem_sim_cutoffs.shape[0])
print "Generating HAC Summary"
print "\tDist Threshold ({}, {}), step={}".format(
hac_dist_min, hac_dist_max, hac_dist_step)
print "\tSim Threshold ({}, {}), step={}".format(
sem_sim_min, sem_sim_max, sem_sim_step)
print "\t{} jobs/event".format(
hac_dist_cutoffs.shape[0] * sem_sim_cutoffs.shape[0])
rsfs = RankedSalienceFilteredSummary()
if not os.path.exists(rsfs.dir_):
os.makedirs(rsfs.dir_)
jobs = []
for event, corpus in dev_data:
for sem_sim_cutoff in sem_sim_cutoffs:
for dist_cutoff in hac_dist_cutoffs:
jobs.append(
(HACFilteredSummary(),
(event, prefix, feature_set,
dist_cutoff, sem_sim_cutoff)))
jobs.append(
(APFilteredSummary(), (event, sem_sim_cutoff)))
for sal_cutoff in sal_cutoffs:
jobs.append(
(APSalienceFilteredSummary(),
(event, prefix, feature_set, sal_cutoff,
sem_sim_cutoff)))
jobs.append(
(APSalTRankSalThreshFilteredSummary(),
(event, prefix, feature_set, sal_cutoff,
sem_sim_cutoff)))
for rank_sim_cutoff in rank_sim_cutoffs:
for sal_cutoff in sal_cutoffs:
jobs.append(
(RankedSalienceFilteredSummary(),
(event, prefix, feature_set, sal_cutoff,
rank_sim_cutoff)))
self.do_work(filter_worker, jobs, **kwargs)
def make(self, event, prefix, feature_set,
min_cluster_size=2, sim_threshold=.2264, center_threshold=1.0):
tsv_path = self.get_tsv_path(event, prefix, feature_set,
center_threshold, sim_threshold)
lvecs = SentenceLatentVectorsResource()
spa = SaliencePredictionAggregator()
apsal = APSalienceSummarizer()
cluster_df = apsal.get_dataframe(event, prefix, feature_set)
#for _, row in cluster_df.iterrows():
# print row['hour'], datetime.utcfromtimestamp(row['timestamp'])
updates = []
Xcache = None
timestamps = sorted(list(cluster_df['timestamp'].unique()))
for timestamp in timestamps:
hour = datetime.utcfromtimestamp(timestamp) - timedelta(hours=1)
lvec_df = lvecs.get_dataframe(event, hour)
lvec_df.drop_duplicates(['stream id', 'sentence id'], inplace=True)
sal_df = spa.get_dataframe(event, hour, prefix, feature_set)
sal_df.drop_duplicates(['stream id', 'sentence id'], inplace=True)
clusters = cluster_df[cluster_df['timestamp'] == timestamp].copy()
clusters.sort(['stream id', 'sentence id'], inplace=True)
salience = []
for _, row in clusters.iterrows():
sal_pred = sal_df.loc[
(sal_df['stream id'] == row['stream id']) & \
(sal_df['sentence id'] == row['sentence id'])].as_matrix()[:,2:].astype(np.float64).mean()
salience.append(sal_pred)
clusters['salience'] = salience
#clusters.sort(['salie'], inplace=True)
sal_mean = np.mean(salience)
sal_std = np.std(salience)
#print clusters
for _, row in clusters.iterrows():
if row['cluster size'] < min_cluster_size:
continue
vec = lvec_df.loc[
(lvec_df['stream id'] == row['stream id']) & \
(lvec_df['sentence id'] == row['sentence id'])].as_matrix()[:,2:].astype(np.float64)
sal_norm = (row['salience'] - sal_mean) / sal_std
if sal_norm < center_threshold:
continue
if Xcache is None:
Xcache = vec
else:
if np.max(cosine_similarity(Xcache, vec)) >= sim_threshold:
continue
else:
Xcache = np.vstack((Xcache, vec))
updates.append({
'query id': event.query_id[5:],
'system id': 'cunlp',
'run id': 'apsal-time-ranked-sal_{}-sim_{}'.format(
center_threshold, sim_threshold),
'stream id': row['stream id'],
'sentence id': row['sentence id'],
'timestamp': timestamp,
'conf': row['salience'],
'string': row['string']
})
print "Writing", tsv_path, "For ", center_threshold, sim_threshold
df = pd.DataFrame(updates,
columns=["query id", "system id", "run id",
"stream id", "sentence id", "timestamp",
"conf", "string"])
with open(tsv_path, u'w') as f:
df.to_csv(
f, sep='\t', index=False, index_label=False, header=False)