def main():
results = []
res = ArticlesResource()
for ext in ["gold", "goose"]:
for event in cuttsum.events.get_2013_events():
if event.query_id.startswith("TS13"):
corpus = cuttsum.corpora.EnglishAndUnknown2013()
else:
raise Exception()
min_hour = datetime.datetime(datetime.MAXYEAR, 1, 1)
max_hour = datetime.datetime(datetime.MINYEAR, 1, 1)
total = 0
for hour, path, si in res.streamitem_iter(event, corpus, ext):
if hour < min_hour:
min_hour = hour
if hour > max_hour:
max_hour = hour
total += 1
if total == 0:
continue
results.append({"event": event.fs_name(),
"event start": event.list_event_hours()[0],
"event stop": event.list_event_hours()[-1],
"article start": min_hour,
"article stop": max_hour,
"total": total,
"annotator": ext})
df = pd.DataFrame(results,
columns=["event", "annotator", "event start", "event stop",
"article start", "article stop", "total", "annotator"])
print df
def cmd_readstream(args, t, active_events):
import textwrap
from goose import Goose, Configuration
config = Configuration()
config.enable_image_fetching = False
g = Goose(config)
raw_stream = True
for arg in args:
if arg == "articles":
raw_stream = False
for event in active_events:
print event
if event.query_id.startswith("TS13"):
corpus = cuttsum.corpora.EnglishAndUnknown2013()
elif event.query_id.startswith("TS14"):
corpus = cuttsum.corpora.SerifOnly2014()
else:
raise Exception("Bad query id: {}".format(event.query_id))
if raw_stream is True:
from cuttsum.trecdata import SCChunkResource
si_iter = SCChunkResource().streamitem_iter(event, corpus)
else:
from cuttsum.pipeline import ArticlesResource
si_iter = ArticlesResource().streamitem_iter(event, corpus)
for hour, path, si in si_iter:
if si.body.clean_visible is not None:
print si.stream_id
try:
text_height = t.height - 4
#n_chars = t.
article = g.extract(raw_html=si.body.clean_html)
lines = textwrap.wrap(article.cleaned_text)
idx = 0
while 1:
print t.clear
print "hour:", hour
print "title:", article.title
print "article:"
print "\n".join(lines[idx:idx + text_height])
#print article.cleaned_text
with t.cbreak():
char = t.inkey()
if char == "i" and idx > 0:
idx -= 1 #idx - 1 if idx > 0 else 0
elif char == "k" and idx + text_height < len(
lines):
idx += 1
elif char == "l":
break
except Exception, e:
print e
continue
def main():
results = []
res = ArticlesResource()
for ext in ["gold", "goose"]:
for event in cuttsum.events.get_2013_events():
if event.query_id.startswith("TS13"):
corpus = cuttsum.corpora.EnglishAndUnknown2013()
else:
raise Exception()
min_hour = datetime.datetime(datetime.MAXYEAR, 1, 1)
max_hour = datetime.datetime(datetime.MINYEAR, 1, 1)
total = 0
for hour, path, si in res.streamitem_iter(event, corpus, ext):
if hour < min_hour:
min_hour = hour
if hour > max_hour:
max_hour = hour
total += 1
if total == 0:
continue
results.append({
"event": event.fs_name(),
"event start": event.list_event_hours()[0],
"event stop": event.list_event_hours()[-1],
"article start": min_hour,
"article stop": max_hour,
"total": total,
"annotator": ext
})
df = pd.DataFrame(results,
columns=[
"event", "annotator", "event start", "event stop",
"article start", "article stop", "total", "annotator"
])
print df
def do_job_unit(self, event, corpus, unit, **kwargs):
if unit != 0:
raise Exception("Job unit {} out of range".format(unit))
res = ArticlesResource()
thresh = kwargs.get("dedupe-sim-threshold", .8)
extractor = kwargs.get("extractor", "goose")
hasher = FeatureHasher(input_type="pair", non_negative=True)
si_iter = res.streamitem_iter(
event, corpus, extractor)
def to_df(all_ids, all_times, all_matches):
d = []
for ids, times, match in izip(all_ids, all_times, all_matches):
times.sort()
d.append({
"stream ids": ids, "hits": len(ids), "match": match,
"earliest": times[0], "latest": times[-1],
"second": times[1] if len(times) >= 2 else None,
"third": times[2] if len(times) >= 3 else None,
})
return pd.DataFrame(d, columns=["stream ids", "match", "hits",
"earliest", "latest",
"second", "third"])
def query_in_top20(event, df):
text = u"\n".join(df["sent text"].tolist()[:20])
for query in event.query:
if not re.search(query, text, flags=re.I|re.UNICODE):
return False
return True
def make_time(df):
return df["timestamp"].tolist()[0]
def make_counts(df, slimit=20):
counts = defaultdict(int)
for words in df["words"].tolist()[:slimit]:
for word in words:
counts[word.lower()] += 1
return counts
def next_chunk_file(chunk_file_num):
deduped_path_fmt = self.get_deduped_path_fmt(
event, corpus, extractor, threshold=thresh)
deduped_path = deduped_path_fmt.format(
chunk_file_num)
deduped_dir = os.path.dirname(deduped_path)
if not os.path.exists(deduped_dir):
os.makedirs(deduped_dir)
if os.path.exists(deduped_path):
os.remove(deduped_path)
return sc.Chunk(path=deduped_path, mode="wb",
message=corpus.sc_msg())
X = None
chunk_file_num = 1
chunk = next_chunk_file(chunk_file_num)
for hour, path, si in si_iter:
df = si2df(si, extractor=extractor)
counts = make_counts(df)
x = hasher.transform([counts.items()])
x.shape = (1, hasher.n_features)
if X is None:
X = x
times = [[make_time(df)]]
ids = [[si.stream_id]]
matches = [query_in_top20(event, df)]
chunk.add(si)
else:
K = cosine_similarity(X, x)
k_argmax = K.argmax()
if K[k_argmax] < thresh:
X = vstack([X, x])
times.append([make_time(df)])
ids.append([si.stream_id])
matches.append(query_in_top20(event, df))
if X.shape[0] % 1000 == 0:
chunk.close()
chunk_file_num += 1
chunk = next_chunk_file(chunk_file_num)
chunk.add(si)
else:
times[k_argmax].append(make_time(df))
ids[k_argmax].append(si.stream_id)
chunk.close()
df = to_df(ids, times, matches)
print df
stats_path = self.get_stats_path(
event, corpus, extractor, thresh)
with open(stats_path, "w") as f:
df.to_csv(f, index=False, sep="\t")
def do_job_unit(self, event, corpus, unit, **kwargs):
if unit != 0:
raise Exception("unit of work out of bounds!")
extractor = kwargs.get("extractor", "gold")
soft_match = kwargs.get("soft_match", False)
budget = kwargs.get("budget", 25)
output_path_prefix = self.get_path_prefix(event, corpus, extractor,
budget, soft_match)
## Set up summarizer ###
# This is the monotone submodular objective function (basically
# nugget coverage).
def f_of_A(system, A, V_min_A, e, input_df, ndarray_data):
return len(
set([
nugget for nuggets in input_df.ix[A, "nuggets"].tolist()
for nugget in nuggets
]))
system = sumpy.system.MonotoneSubmodularBasic(f_of_A=f_of_A, k=budget)
# Get gold matchings for oracle.
articles = ArticlesResource()
all_matches = cuttsum.judgements.get_merged_dataframe()
matches = all_matches[all_matches["query id"] == event.query_id]
# Set up soft matching if we are using it.
if soft_match is True:
from cuttsum.classifiers import NuggetClassifier
classify_nuggets = NuggetClassifier().get_classifier(event)
if event.query_id.startswith("TS13"):
judged = cuttsum.judgements.get_2013_updates()
judged = judged[judged["query id"] == event.query_id]
judged_uids = set(judged["update id"].tolist())
else:
raise Exception("Bad corpus!")
# All sentences containing nuggets will go in all_df.
all_df = []
# Pull out articles with nuggets.
for hour, path, si in articles.streamitem_iter(event, corpus,
extractor):
# Convert stream item to dataframe and add gold label nuggets.
df = si2df(si, extractor=extractor)
df["nuggets"] = df["update id"].apply(lambda x: set(matches[
matches["update id"] == x]["nugget id"].tolist()))
# Perform soft nugget matching on unjudged sentences.
if soft_match is True:
### NOTE BENE: geting an array of indices to index unjudged
# sentences so I can force pandas to return a view and not a
# copy.
I = np.where(
df["update id"].apply(lambda x: x not in judged_uids))[0]
unjudged = df[df["update id"].apply(
lambda x: x not in judged_uids)]
unjudged_sents = unjudged["sent text"].tolist()
assert len(unjudged_sents) == I.shape[0]
df.loc[I, "nuggets"] = classify_nuggets(unjudged_sents)
# Add sentences with nuggets to final set for summarzing
df = df[df["nuggets"].apply(len) > 0]
all_df.append(df)
# Collect all dataframes into one and reset index (ALWAYS RESET
# THE INDEX because pandas hates me.)
all_df = pd.concat(all_df)
all_df.reset_index(inplace=True)
summary = system.summarize(all_df)
F_of_S = len(
set(n for ns in summary._df["nuggets"].tolist() for n in ns))
#print "F(S)", F_of_S
#print "summary nuggets"
sum_nuggets = list(
set(n for ns in summary._df["nuggets"].tolist() for n in ns))
sum_nuggets.sort()
print sum_nuggets
possible_nuggets = list(
set(n for ns in all_df["nuggets"].tolist() for n in ns))
possible_nuggets.sort()
print possible_nuggets
print len(possible_nuggets)
event_nuggets = set(matches["nugget id"].tolist())
total_nuggets = len(event_nuggets)
timestamp = int(si.stream_id.split("-")[0])
output_df = pd.DataFrame(
[
{
"Cum. F(S)": F_of_S,
"F(S)": F_of_S,
"UB no const.":
len(possible_nuggets), # total_nuggets,
"budget": budget,
"Tot. Updates": len(summary._df),
"event title": event.fs_name(),
"timestamp": timestamp,
"query id": event.query_id
},
],
columns=[
"timestamp",
"query id",
"event title",
"Cum. F(S)",
"F(S)",
"UB no const.",
"Tot. Updates",
"budget",
])
parent = os.path.dirname(output_path_prefix)
if not os.path.exists(parent):
os.makedirs(parent)
stats_path = output_path_prefix + ".stats.tsv"
updates_path = output_path_prefix + ".updates.tsv"
with open(stats_path, "w") as f:
output_df.to_csv(f, sep="\t", index=False)
summary._df["sent text"] = summary._df["sent text"].apply(
lambda x: x.encode("utf-8"))
with open(updates_path, "w") as f:
summary._df[["timestamp", "update id",
"sent text"]].sort(["update id"]).to_csv(f,
sep="\t",
index=False)
def do_job_unit(self, event, corpus, unit, **kwargs):
if unit != 0:
raise Exception("unit of work out of bounds!")
extractor = kwargs.get("extractor", "gold")
soft_match = kwargs.get("soft_match", False)
budget = kwargs.get("budget", 25)
output_path_prefix = self.get_path_prefix(event, corpus, extractor,
budget, soft_match)
## Set up summarizer ###
def f_of_A(system, A, V_min_A, e, input_df, ndarray_data):
return len(
set([
nugget for nuggets in input_df.ix[A, "nuggets"].tolist()
for nugget in nuggets
]))
system = sumpy.system.MonotoneSubmodularBasic(f_of_A=f_of_A, k=budget)
# Collect all previously collected nuggets here.
nugget_cache = set()
# Get gold matchings for oracle.
articles = ArticlesResource()
all_matches = cuttsum.judgements.get_merged_dataframe()
matches = all_matches[all_matches["query id"] == event.query_id]
# Set up soft matching if we are using it.
if soft_match is True:
from cuttsum.classifiers import NuggetClassifier
classify_nuggets = NuggetClassifier().get_classifier(event)
if event.query_id.startswith("TS13"):
judged = cuttsum.judgements.get_2013_updates()
judged = judged[judged["query id"] == event.query_id]
judged_uids = set(judged["update id"].tolist())
else:
raise Exception("Bad corpus!")
# Collect stats for each document here.
stats = []
# Aggregate summaries in summary_df.
summary_df = []
cum_F_of_S = 0
all_seen_nuggets = set()
# event_nuggets = set(matches["nugget id"].tolist())
# total_nuggets = len(event_nuggets)
total_updates = 0
# Pull out articles with nuggets.
for hour, path, si in articles.streamitem_iter(event, corpus,
extractor):
print hour, si.stream_id
# Convert stream item to dataframe and add gold label nuggets.
df = si2df(si, extractor=extractor)
df["nuggets"] = df["update id"].apply(lambda x: set(matches[
matches["update id"] == x]["nugget id"].tolist()))
# Perform soft nugget matching on unjudged sentences.
if soft_match is True:
### NOTE BENE: geting an array of indices to index unjudged
# sentences so I can force pandas to return a view and not a
# copy.
I = np.where(
df["update id"].apply(lambda x: x not in judged_uids))[0]
unjudged = df[df["update id"].apply(
lambda x: x not in judged_uids)]
unjudged_sents = unjudged["sent text"].tolist()
assert len(unjudged_sents) == I.shape[0]
df.loc[I, "nuggets"] = classify_nuggets(unjudged_sents)
# Remove nuggets from dataframe if we have already collected them in
# the cache. The scoring function should ignore these.
df = df[df["nuggets"].apply(len) > 0]
all_seen_nuggets.update(
set(n for ns in df["nuggets"].tolist() for n in ns))
df["nuggets"] = df["nuggets"].apply(
lambda x: x.difference(nugget_cache))
if len(df) == 0:
continue
# Run summarizer on current document and update stats about it.
summary = system.summarize(df)
summary_nuggets = set(n for ns in summary._df["nuggets"].tolist()
for n in ns)
nugget_cache.update(summary_nuggets)
system.k -= len(summary._df)
F_of_S = len(summary_nuggets)
cum_F_of_S += F_of_S
total_updates += len(summary._df)
timestamp = int(si.stream_id.split("-")[0])
stats.append({
"Cum. F(S)": cum_F_of_S,
"F(S)": F_of_S,
"UB no const.": len(all_seen_nuggets),
"budget": budget,
"Tot. Updates": total_updates,
"event title": event.fs_name(),
"timestamp": timestamp,
"query id": event.query_id,
})
summary_df.append(summary._df)
if system.k <= 0:
print "Budget exceeded!"
break
output_df = pd.DataFrame(stats,
columns=[
"timestamp",
"query id",
"event title",
"Cum. F(S)",
"F(S)",
"UB no const.",
"Tot. Updates",
"budget",
])
# Write stats and updates to file.
parent = os.path.dirname(output_path_prefix)
if not os.path.exists(parent):
os.makedirs(parent)
stats_path = output_path_prefix + ".stats.tsv"
updates_path = output_path_prefix + ".updates.tsv"
with open(stats_path, "w") as f:
output_df.to_csv(f, sep="\t", index=False)
summary_df = pd.concat(summary_df)
summary_df["sent text"] = summary_df["sent text"].apply(
lambda x: x.encode("utf-8"))
with open(updates_path, "w") as f:
summary_df[["timestamp", "update id",
"sent text"]].sort(["update id"]).to_csv(f,
sep="\t",
index=False)
import cuttsum.events
import cuttsum.corpora
from cuttsum.pipeline import ArticlesResource
import numpy as np
from scipy import linalg
event = cuttsum.events.get_events()[0]
corpus = cuttsum.corpora.EnglishAndUnknown2013()
extractor = "gold"
res = ArticlesResource()
from sklearn.feature_extraction import DictVectorizer
def extract_sentence_features_gold(df, vec):
all_nuggets = set(
n for ns in df["nuggets"].tolist() for n in ns)
X_dict = df["nuggets"].apply(
lambda x: {key: 1 if key in x else -1 for key in all_nuggets}).tolist()
#vec = DictVectorizer(sparse=False)
X = vec.fit_transform(X_dict)
return X, vec
w = None
k = 5
m = 10
cache = {}
found_nuggets = set()
possible_nuggets = set(['VMTS13.01.064', 'VMTS13.01.058', 'VMTS13.01.056', 'VMTS13.01.055', 'VMTS13.01.054', 'VMTS13.01.052', 'VMTS13.01.051', 'VMTS13.01.050', 'VMTS13.01.062', 'VMTS13.01.088', 'VMTS13.01.077', 'VMTS13.01.065', 'VMTS13.01.078', 'VMTS13.01.086', 'VMTS13.01.069', 'VMTS13.01.099' "VMTS13.01.060", 'VMTS13.01.092', 'VMTS13.01.091', 'VMTS13.01.090', 'VMTS13.01.097', 'VMTS13.01.059', 'VMTS13.01.099', 'VMTS13.01.071', 'VMTS13.01.070', 'VMTS13.01.073', 'VMTS13.01.061', 'VMTS13.01.075', 'VMTS13.01.067', 'VMTS13.01.076', 'VMTS13.01.104', 'VMTS13.01.068', 'VMTS13.01.106', 'VMTS13.01.103', 'VMTS13.01.063', 'VMTS13.01.060', 'VMTS13.01.084', 'VMTS13.01.085', 'VMTS13.01.087', 'VMTS13.01.081', 'VMTS13.01.074'])
def do_job_unit(self, event, corpus, unit, **kwargs):
if unit != 0:
raise Exception("unit of work out of bounds!")
extractor = kwargs.get("extractor", "gold")
soft_match = kwargs.get("soft_match", False)
budget = kwargs.get("budget", 25)
output_path_prefix = self.get_path_prefix(
event, corpus, extractor, budget, soft_match)
## Set up summarizer ###
# This is the monotone submodular objective function (basically
# nugget coverage).
def f_of_A(system, A, V_min_A, e, input_df, ndarray_data):
return len(
set([nugget for nuggets in input_df.ix[A, "nuggets"].tolist()
for nugget in nuggets]))
system = sumpy.system.MonotoneSubmodularBasic(f_of_A=f_of_A, k=budget)
# Get gold matchings for oracle.
articles = ArticlesResource()
all_matches = cuttsum.judgements.get_merged_dataframe()
matches = all_matches[all_matches["query id"] == event.query_id]
# Set up soft matching if we are using it.
if soft_match is True:
from cuttsum.classifiers import NuggetClassifier
classify_nuggets = NuggetClassifier().get_classifier(event)
if event.query_id.startswith("TS13"):
judged = cuttsum.judgements.get_2013_updates()
judged = judged[judged["query id"] == event.query_id]
judged_uids = set(judged["update id"].tolist())
else:
raise Exception("Bad corpus!")
# All sentences containing nuggets will go in all_df.
all_df = []
# Pull out articles with nuggets.
for hour, path, si in articles.streamitem_iter(
event, corpus, extractor):
# Convert stream item to dataframe and add gold label nuggets.
df = si2df(si, extractor=extractor)
df["nuggets"] = df["update id"].apply(
lambda x: set(
matches[matches["update id"] == x]["nugget id"].tolist()))
# Perform soft nugget matching on unjudged sentences.
if soft_match is True:
### NOTE BENE: geting an array of indices to index unjudged
# sentences so I can force pandas to return a view and not a
# copy.
I = np.where(
df["update id"].apply(lambda x: x not in judged_uids))[0]
unjudged = df[
df["update id"].apply(lambda x: x not in judged_uids)]
unjudged_sents = unjudged["sent text"].tolist()
assert len(unjudged_sents) == I.shape[0]
df.loc[I, "nuggets"] = classify_nuggets(unjudged_sents)
# Add sentences with nuggets to final set for summarzing
df = df[df["nuggets"].apply(len) > 0]
all_df.append(df)
# Collect all dataframes into one and reset index (ALWAYS RESET
# THE INDEX because pandas hates me.)
all_df = pd.concat(all_df)
all_df.reset_index(inplace=True)
summary = system.summarize(all_df)
F_of_S = len(
set(n for ns in summary._df["nuggets"].tolist() for n in ns))
#print "F(S)", F_of_S
#print "summary nuggets"
sum_nuggets = list(set(
n for ns in summary._df["nuggets"].tolist() for n in ns))
sum_nuggets.sort()
print sum_nuggets
possible_nuggets = list(set(
n for ns in all_df["nuggets"].tolist() for n in ns))
possible_nuggets.sort()
print possible_nuggets
print len(possible_nuggets)
event_nuggets = set(matches["nugget id"].tolist())
total_nuggets = len(event_nuggets)
timestamp = int(si.stream_id.split("-")[0])
output_df = pd.DataFrame(
[{"Cum. F(S)": F_of_S,
"F(S)": F_of_S,
"UB no const.": len(possible_nuggets), # total_nuggets,
"budget": budget,
"Tot. Updates": len(summary._df),
"event title": event.fs_name(),
"timestamp": timestamp,
"query id": event.query_id},],
columns=["timestamp", "query id", "event title", "Cum. F(S)",
"F(S)", "UB no const.",
"Tot. Updates", "budget",])
parent = os.path.dirname(output_path_prefix)
if not os.path.exists(parent):
os.makedirs(parent)
stats_path = output_path_prefix + ".stats.tsv"
updates_path = output_path_prefix + ".updates.tsv"
with open(stats_path, "w") as f:
output_df.to_csv(f, sep="\t", index=False)
summary._df["sent text"] = summary._df["sent text"].apply(
lambda x: x.encode("utf-8"))
with open(updates_path, "w") as f:
summary._df[["timestamp", "update id", "sent text"]].sort(
["update id"]).to_csv(f, sep="\t", index=False)
def do_job_unit(self, event, corpus, unit, **kwargs):
if unit != 0:
raise Exception("unit of work out of bounds!")
extractor = kwargs.get("extractor", "gold")
soft_match = kwargs.get("soft_match", False)
budget = kwargs.get("budget", 25)
output_path_prefix = self.get_path_prefix(
event, corpus, extractor, budget, soft_match)
## Set up summarizer ###
def f_of_A(system, A, V_min_A, e, input_df, ndarray_data):
return len(
set([nugget for nuggets in input_df.ix[A, "nuggets"].tolist()
for nugget in nuggets]))
system = sumpy.system.MonotoneSubmodularBasic(f_of_A=f_of_A, k=budget)
# Collect all previously collected nuggets here.
nugget_cache = set()
# Get gold matchings for oracle.
articles = ArticlesResource()
all_matches = cuttsum.judgements.get_merged_dataframe()
matches = all_matches[all_matches["query id"] == event.query_id]
# Set up soft matching if we are using it.
if soft_match is True:
from cuttsum.classifiers import NuggetClassifier
classify_nuggets = NuggetClassifier().get_classifier(event)
if event.query_id.startswith("TS13"):
judged = cuttsum.judgements.get_2013_updates()
judged = judged[judged["query id"] == event.query_id]
judged_uids = set(judged["update id"].tolist())
else:
raise Exception("Bad corpus!")
# Collect stats for each document here.
stats = []
# Aggregate summaries in summary_df.
summary_df = []
cum_F_of_S = 0
all_seen_nuggets = set()
# event_nuggets = set(matches["nugget id"].tolist())
# total_nuggets = len(event_nuggets)
total_updates = 0
# Pull out articles with nuggets.
for hour, path, si in articles.streamitem_iter(
event, corpus, extractor):
print hour, si.stream_id
# Convert stream item to dataframe and add gold label nuggets.
df = si2df(si, extractor=extractor)
df["nuggets"] = df["update id"].apply(
lambda x: set(
matches[matches["update id"] == x]["nugget id"].tolist()))
# Perform soft nugget matching on unjudged sentences.
if soft_match is True:
### NOTE BENE: geting an array of indices to index unjudged
# sentences so I can force pandas to return a view and not a
# copy.
I = np.where(
df["update id"].apply(lambda x: x not in judged_uids))[0]
unjudged = df[
df["update id"].apply(lambda x: x not in judged_uids)]
unjudged_sents = unjudged["sent text"].tolist()
assert len(unjudged_sents) == I.shape[0]
df.loc[I, "nuggets"] = classify_nuggets(unjudged_sents)
# Remove nuggets from dataframe if we have already collected them in
# the cache. The scoring function should ignore these.
df = df[df["nuggets"].apply(len) > 0]
all_seen_nuggets.update(
set(n for ns in df["nuggets"].tolist() for n in ns))
df["nuggets"] = df["nuggets"].apply(
lambda x: x.difference(nugget_cache))
if len(df) == 0:
continue
# Run summarizer on current document and update stats about it.
summary = system.summarize(df)
summary_nuggets = set(n for ns in summary._df["nuggets"].tolist()
for n in ns)
nugget_cache.update(summary_nuggets)
system.k -= len(summary._df)
F_of_S = len(summary_nuggets)
cum_F_of_S += F_of_S
total_updates += len(summary._df)
timestamp = int(si.stream_id.split("-")[0])
stats.append({
"Cum. F(S)": cum_F_of_S,
"F(S)": F_of_S,
"UB no const.": len(all_seen_nuggets),
"budget": budget,
"Tot. Updates": total_updates,
"event title": event.fs_name(),
"timestamp": timestamp,
"query id": event.query_id,
})
summary_df.append(summary._df)
if system.k <= 0:
print "Budget exceeded!"
break
output_df = pd.DataFrame(stats,
columns=["timestamp", "query id", "event title",
"Cum. F(S)", "F(S)", "UB no const.",
"Tot. Updates", "budget",])
# Write stats and updates to file.
parent = os.path.dirname(output_path_prefix)
if not os.path.exists(parent):
os.makedirs(parent)
stats_path = output_path_prefix + ".stats.tsv"
updates_path = output_path_prefix + ".updates.tsv"
with open(stats_path, "w") as f:
output_df.to_csv(f, sep="\t", index=False)
summary_df = pd.concat(summary_df)
summary_df["sent text"] = summary_df["sent text"].apply(
lambda x: x.encode("utf-8"))
with open(updates_path, "w") as f:
summary_df[["timestamp", "update id", "sent text"]].sort(
["update id"]).to_csv(f, sep="\t", index=False)
import pandas as pd
from datetime import datetime, timedelta
from collections import defaultdict
import matplotlib.pylab as plt
plt.style.use('ggplot')
pd.set_option('display.width', 200)
import locale
locale.setlocale(locale.LC_ALL, 'en_US.UTF8')
def unix_time(dt):
epoch = datetime.utcfromtimestamp(0)
delta = dt - epoch
return int(delta.total_seconds())
articles_res = ArticlesResource()
#ded_articles_res = DedupedArticlesResource()
data = []
events = []
for event in cuttsum.events.get_events():
if event.query_num == 7: continue
if event.query_num > 25: continue
events.append(event)
corpus = cuttsum.corpora.get_raw_corpus(event)
hours = event.list_event_hours()
hour2goose = defaultdict(int)
for hour in hours:
from datetime import datetime, timedelta
from collections import defaultdict
import matplotlib.pylab as plt
plt.style.use('ggplot')
pd.set_option('display.width', 200)
import locale
locale.setlocale(locale.LC_ALL, 'en_US.UTF8')
def unix_time(dt):
epoch = datetime.utcfromtimestamp(0)
delta = dt - epoch
return int(delta.total_seconds())
articles_res = ArticlesResource()
#ded_articles_res = DedupedArticlesResource()
data = []
events = []
for event in cuttsum.events.get_events():
if event.query_num == 7: continue
if event.query_num > 25: continue
events.append(event)
corpus = cuttsum.corpora.get_raw_corpus(event)
hours = event.list_event_hours()
hour2goose = defaultdict(int)
for hour in hours:
path = articles_res.get_chunk_path(event, "goose", hour)
if path is None:
