def geo_tagger():
return tagnews.GeoCoder()
crimetags = tagnews.CrimeTags()
article_text = (
'The homicide occurred at the 1700 block of S. Halsted Ave.'
' It happened just after midnight. Another person was killed at the'
' intersection of 55th and Woodlawn, where a lone gunman')
print(crimetags.tagtext_proba(article_text))
# HOMI 0.739159
# VIOL 0.146943
# GUNV 0.134798
print(crimetags.tagtext(article_text, prob_thresh=0.5))
# ['HOMI']
### Must run this from terminal to train geo model first
### cd lib
### python -m tagnews.geoloc.models.lstm.save_model
geoextractor = tagnews.GeoCoder()
prob_out = geoextractor.extract_geostring_probs(article_text)
print(list(zip(*prob_out)))
# [..., ('at', 0.0044685714), ('the', 0.005466637), ('1700', 0.7173856),
# ('block', 0.81395197), ('of', 0.82227415), ('S.', 0.7940061),
# ('Halsted', 0.70529455), ('Ave.', 0.60538065), ...]
geostrings = geoextractor.extract_geostrings(article_text, prob_thresh=0.5)
print(geostrings)
# [['1700', 'block', 'of', 'S.', 'Halsted', 'Ave.'], ['55th', 'and', 'Woodlawn,']]
coords, scores = geoextractor.lat_longs_from_geostring_lists(geostrings)
print(coords)
# lat long
# 0 41.859021 -87.646934
# 1 41.794816 -87.597422
print(scores
) # confidence in the lat/longs as returned by pelias, higher is better
def setup_class(cls):
cls.model = tagnews.GeoCoder()
def setup_method(cls):
cls.model = tagnews.GeoCoder()