def test_no_locations(self):
'''
Test that the extractor works with an empty list of locations.
'''
extractor = geoextract.NameExtractor()
pipeline = geoextract.Pipeline([], extractors=[extractor])
assert pipeline.extract('foobar') == []
def test_app_creation(self):
'''
Test creating a web app from a pipeline.
'''
pipeline = geoextract.Pipeline([])
app = pipeline.create_app()
assert hasattr(app, 'run')
def test_no_validation(self):
'''
Test disabled validation.
'''
extractor = FakeExtractor([(0, 1, {'name': 'a'})])
pipeline = geoextract.Pipeline([], extractors=[extractor],
validator=False)
assert pipeline.extract('does not matter') == [{'name': 'a'}]
def test_extractors(self):
'''
Test that extractors are called correctly.
'''
extractor1 = FakeExtractor([(0, 1, {'name': 'foo'})])
extractor2 = FakeExtractor([(1, 1, {'name': 'bar'})])
pipeline = geoextract.Pipeline([], extractors=[extractor1, extractor2])
results = pipeline.extract('does not matter')
assert sorted(r['name'] for r in results) == ['bar', 'foo']
def test_locations(self):
'''
Test that locations are correctly converted to a dict.
'''
pipeline = geoextract.Pipeline([location1, location2])
locations = pipeline.locations
assert locations['foo'] is location1
assert locations['bar'] is location2
assert len(locations) == 2
def test_no_splitting(self):
'''
Test disabled splitting.
'''
extractor = mock.Mock()
extractor.extract = mock.Mock()
extractor.extract.return_value = []
pipeline = geoextract.Pipeline([], extractors=[extractor],
splitter=False)
pipeline.extract('white space')
extractor.extract.assert_called_once_with('white space')
def test_normalized_extractor_input(self):
'''
Test that extractor input is normalized.
'''
extractor = mock.Mock()
extractor.extract = mock.Mock()
extractor.extract.return_value = []
pipeline = geoextract.Pipeline([], extractors=[extractor],
normalizer=UpperNormalizer())
pipeline.extract('foo')
extractor.extract.assert_called_once_with('FOO')
def test_normalized_names(self):
'''
Test that location names are correctly normalized.
'''
pipeline = geoextract.Pipeline([location1, location2],
normalizer=UpperNormalizer())
names = pipeline.normalized_names
assert names['FOO'] is location1
assert names['BAR'] is location2
assert names['BAZINGA'] is location2
assert len(names) == 3
def test_no_normalizer(self):
'''
Test disabled normalization.
'''
extractor = mock.Mock()
extractor.extract = mock.Mock()
extractor.extract.return_value = [(0, 1, {'name': 'A B'})]
pipeline = geoextract.Pipeline([{'name': 'A B'}],
extractors=[extractor],
normalizer=False)
results = pipeline.extract('NO_NORMALIZATION--')
extractor.extract.assert_called_once_with('NO_NORMALIZATION--')
assert results == [{'name': 'A B'}]
def test_splitting(self):
'''
Test splitting of documents.
'''
class MockSplitter(geoextract.Splitter):
def split(self, s):
return s
extractor = mock.Mock()
extractor.extract = mock.Mock()
extractor.extract.return_value = []
pipeline = geoextract.Pipeline([], extractors=[extractor],
splitter=MockSplitter())
pipeline.extract('foo')
extractor.extract.assert_has_calls(
[mock.call('f'), mock.call('o'), mock.call('o')]
)
def test_validation(self):
'''
Test validation of results.
'''
class MockValidator(geoextract.Validator):
def validate(self, location):
return location['name'] == 'a'
extractor = FakeExtractor([
(0, 1, {'name': 'a'}),
(1, 1, {'name': 'b'}),
])
pipeline = geoextract.Pipeline([], extractors=[extractor],
validator=MockValidator())
results = pipeline.extract('does not matter')
assert len(results) == 1
assert results[0]['name'] == 'a'
def test_name_denormalization(self):
'''
Test that names in results are denormalized.
'''
locations = [
{'name': 'a-street'},
{'name': 'a-city'},
{'name': 'a-name'},
]
normalizer = UpperNormalizer()
result = (0, 0, {'name': 'A-NAME', 'street': 'A-STREET',
'city': 'A-CITY'})
pipeline = geoextract.Pipeline(locations, normalizer=normalizer,
extractors=[FakeExtractor([result])])
extracted = pipeline.extract('does not matter')
assert extracted[0]['name'] == 'a-name'
assert extracted[0]['street'] == 'a-street'
assert extracted[0]['city'] == 'a-city'
def test_postprocessing(self):
'''
Test postprocessing of results.
'''
class MockPostprocessor(geoextract.Postprocessor):
def postprocess(self, location):
if location['name'] == 'a':
location['foo'] = 'bar'
return location
else:
return False
extractor = FakeExtractor([
(0, 1, {'name': 'a'}),
(1, 1, {'name': 'b'}),
])
pipeline = geoextract.Pipeline([], extractors=[extractor],
postprocessors=[MockPostprocessor()])
results = pipeline.extract('does not matter')
assert len(results) == 1
assert results[0] == {'name': 'a', 'foo': 'bar'}
def test_component_setup(self):
'''
Test that components are setup correctly.
'''
normalizer = mock.Mock()
extractor1 = mock.Mock()
extractor2 = mock.Mock()
validator = mock.Mock()
splitter = mock.Mock()
postprocessor1 = mock.Mock()
postprocessor2 = mock.Mock()
geoextract.Pipeline([], extractors=[extractor1, extractor2],
validator=validator, normalizer=normalizer,
splitter=splitter, postprocessors=[postprocessor1,
postprocessor2])
assert normalizer.setup.called
assert extractor1.setup.called
assert extractor2.setup.called
assert validator.setup.called
assert splitter.setup.called
assert postprocessor1.setup.called
assert postprocessor2.setup.called
def test_duplicate_removal(self):
'''
Test removal of duplicate results.
'''
keys = ['street', 'house_number', 'postcode', 'city']
for subkeys in subsets(keys):
subkeys.append('name')
loc1 = {subkey: subkey for subkey in subkeys}
loc2 = loc1.copy() # Equal to loc1
loc3 = loc1.copy()
loc3['foo'] = 'bar' # Equal to loc1 because other keys are ignored
loc4 = loc1.copy()
loc4[subkeys[0]] = 'x' # Not equal
extractor = FakeExtractor([
(0, 1, loc1),
(1, 1, loc2),
(2, 1, loc3),
(3, 1, loc4),
])
pipeline = geoextract.Pipeline([], extractors=[extractor])
results = pipeline.extract('does not matter')
assert sort_as_json(results) == sort_as_json([loc1, loc4])
def test_pruning_of_overlapping_results(self):
'''
Test that overlapping results are pruned.
'''
# a
# bb
# c
# ddd
# eeee
# ff
# gg
extractor = FakeExtractor([
(0, 1, {'name': 'a'}),
(1, 2, {'name': 'b'}),
(2, 1, {'name': 'c'}),
(2, 3, {'name': 'd'}),
(1, 4, {'name': 'e'}),
(5, 2, {'name': 'f'}),
(4, 2, {'name': 'g'}),
])
pipeline = geoextract.Pipeline([], extractors=[extractor])
results = pipeline.extract('does not matter')
assert sorted(r['name'] for r in results) == ['a', 'e', 'f', 'g']
key_filter_postprocessor = geoextract.KeyFilterPostprocessor(KEYS_TO_KEEP)
#
# PIPELINE CONSTRUCTION
#
# A pipeline connects all the different components.
#
# Here we're using custom extractors and a custom normalizer. We could also
# provide our own code for splitting a document into chunks and for validation,
# but for simplicity we'll use the default implementations in these cases.
pipeline = geoextract.Pipeline(
locations,
extractors=[pattern_extractor, name_extractor],
normalizer=normalizer,
postprocessors=[key_filter_postprocessor],
)
#
# COMMAND LINE INTERFACE
#
# This example can be used to either extract locations from a file specified on
# the command line or (if no additional argument is given) to start a web
# server which provides location extraction as a web service.
if __name__ == '__main__':
import io
import json
def extract_found_locations(text, bodies=None):
"""
:type text: str
:type bodies: list of Body
:return: list
"""
search_for = create_geoextract_data(bodies)
#
# STRING NORMALIZATION
#
# Strings must be normalized before searching and matching them. This includes
# technical normalization (e.g. Unicode normalization), linguistic
# normalization (e.g. stemming) and content normalization (e.g. synonym
# handling).
normalizer = geoextract.BasicNormalizer(subs=[(r'str\b', 'strasse')],
stem='german')
#
# NAMES
#
# Many places can be referred to using just their name, for example specific
# buildings (e.g. the Brandenburger Tor), streets (Hauptstraße) or other
# points of interest. These can be extracted using the ``NameExtractor``.
#
# Note that extractor will automatically receive the (normalized) location
# names from the pipeline we construct later, so there's no need to explicitly
# pass them to the constructor.
name_extractor = geoextract.NameExtractor()
#
# PATTERNS
#
# For locations that are notated using a semi-structured format (addresses)
# the ``PatternExtractor`` is a good choice. It looks for matches of regular
# expressions.
#
# The patterns should have named groups, their sub-matches will be
# returned in the extracted locations.
address_pattern = re.compile(r'''
(?P<street>[^\W\d_](?:[^\W\d_]|\s)*[^\W\d_])
\s+
(?P<house_number>([1-9]\d*)[\w-]*)
(
\s+
(
(?P<postcode>\d{5})
\s+
)?
(?P<city>([^\W\d_]|-)+)
)?
''',
flags=re.UNICODE | re.VERBOSE)
pattern_extractor = geoextract.PatternExtractor([address_pattern])
#
# POSTPROCESSING
#
# Once locations are extracted you might want to postprocess them, for example
# to remove certain attributes that are useful for validation but are not
# intended for publication. Or you may want to remove a certain address that's
# printed in the footer of all the documents you're processing.
#
# GeoExtract allows you to do this by using one or more postprocessors. In this
# example we will remove all but a few keys from our location dicts.
keys_to_keep = ['name', 'street', 'house_number', 'postcode', 'city']
key_filter_postprocessor = geoextract.KeyFilterPostprocessor(keys_to_keep)
#
# PIPELINE CONSTRUCTION
#
# A pipeline connects all the different components.
#
# Here we're using custom extractors and a custom normalizer. We could also
# provide our own code for splitting a document into chunks and for validation,
# but for simplicity we'll use the default implementations in these cases.
pipeline = geoextract.Pipeline(
search_for,
extractors=[pattern_extractor, name_extractor],
normalizer=normalizer,
postprocessors=[key_filter_postprocessor],
)
return pipeline.extract(text)