def test_modify_search_filter_hook(self):
# Prep an empty result.
mock_api = self.generate_mock_api()
# With an empty recommendation result, the Filter is set up
# to return nothing.
lane = RecommendationLane(
self._default_library, self.work, "", novelist_api=mock_api
)
filter = Filter()
assert False == filter.match_nothing
modified = lane.modify_search_filter_hook(filter)
assert modified == filter
assert True == filter.match_nothing
# When there are recommendations, the Filter is modified to
# match only those ISBNs.
i1 = self._identifier()
i2 = self._identifier()
lane.recommendations = [i1, i2]
filter = Filter()
assert [] == filter.identifiers
modified = lane.modify_search_filter_hook(filter)
assert modified == filter
assert [i1, i2] == filter.identifiers
assert False == filter.match_nothing
def test_works_query_with_source_audience(self):
# If the lane is created with a source audience, it filters the
# recommendations appropriately.
works = self.sample_works_for_each_audience()
[children, ya, adult, adults_only] = works
recommendations = list()
for work in works:
recommendations.append(work.license_pools[0].identifier)
expected = {
Classifier.AUDIENCE_CHILDREN : [children],
Classifier.AUDIENCE_YOUNG_ADULT : [children, ya],
Classifier.AUDIENCE_ADULTS_ONLY : works
}
for audience, results in expected.items():
self.work.audience = audience
SessionManager.refresh_materialized_views(self._db)
mock_api = self.generate_mock_api()
lane = RecommendationLane(
self._default_library, self.work, '', novelist_api=mock_api
)
lane.recommendations = recommendations
self.assert_works_queries(lane, results)
def test_modify_search_filter_hook(self):
# Prep an empty result.
mock_api = self.generate_mock_api()
# With an empty recommendation result, the Filter is set up
# to return nothing.
lane = RecommendationLane(self._default_library,
self.work,
'',
novelist_api=mock_api)
filter = Filter()
eq_(False, filter.match_nothing)
modified = lane.modify_search_filter_hook(filter)
eq_(modified, filter)
eq_(True, filter.match_nothing)
# When there are recommendations, the Filter is modified to
# match only those ISBNs.
i1 = self._identifier()
i2 = self._identifier()
lane.recommendations = [i1, i2]
filter = Filter()
eq_([], filter.identifiers)
modified = lane.modify_search_filter_hook(filter)
eq_(modified, filter)
eq_([i1, i2], filter.identifiers)
eq_(False, filter.match_nothing)
def test_works_query_with_source_language(self):
# Prepare a number of works with different languages.
# TODO: Setting a data source name is necessary because
# Gutenberg books get filtered out when children or ya
# is one of the lane's audiences.
eng = self._work(with_license_pool=True, language='eng', data_source_name=DataSource.OVERDRIVE)
fre = self._work(with_license_pool=True, language='fre', data_source_name=DataSource.OVERDRIVE)
spa = self._work(with_license_pool=True, language='spa', data_source_name=DataSource.OVERDRIVE)
SessionManager.refresh_materialized_views(self._db)
# They're all returned as recommendations from NoveList Select.
recommendations = list()
for work in [eng, fre, spa]:
recommendations.append(work.license_pools[0].identifier)
# But only the work that matches the source work is included.
mock_api = self.generate_mock_api()
lane = RecommendationLane(self._default_library, self.work, '', novelist_api=mock_api)
lane.recommendations = recommendations
self.assert_works_queries(lane, [eng])
# It doesn't matter the language.
self.work.presentation_edition.language = 'fre'
SessionManager.refresh_materialized_views(self._db)
mock_api = self.generate_mock_api()
lane = RecommendationLane(self._default_library, self.work, '', novelist_api=mock_api)
lane.recommendations = recommendations
self.assert_works_queries(lane, [fre])
def test_works_query_with_source_audience(self):
# If the lane is created with a source audience, it filters the
# recommendations appropriately.
works = self.sample_works_for_each_audience()
[children, ya, adult, adults_only] = works
recommendations = list()
for work in works:
recommendations.append(work.license_pools[0].identifier)
expected = {
Classifier.AUDIENCE_CHILDREN : [children],
Classifier.AUDIENCE_YOUNG_ADULT : [children, ya],
Classifier.AUDIENCE_ADULTS_ONLY : works
}
for audience, results in expected.items():
self.work.audience = audience
SessionManager.refresh_materialized_views(self._db)
mock_api = self.generate_mock_api()
lane = RecommendationLane(
self._default_library, self.work, '', novelist_api=mock_api
)
lane.recommendations = recommendations
self.assert_works_queries(lane, results)
def test_works_query(self):
# Prep an empty result.
mock_api = self.generate_mock_api()
# With an empty recommendation result, the lane is empty.
lane = RecommendationLane(self._db, self.lp, '', novelist_api=mock_api)
eq_(None, lane.works())
eq_(None, lane.materialized_works())
# Resulting recommendations are returned when available, though.
result = self._work(with_license_pool=True)
lane.recommendations = [result.license_pools[0].identifier]
SessionManager.refresh_materialized_views(self._db)
self.assert_works_queries(lane, [result])
def test_overview_facets(self):
# A FeaturedFacets object is adapted to a Facets object with
# specific settings.
featured = FeaturedFacets(0.44, entrypoint=AudiobooksEntryPoint)
lane = RecommendationLane(
self._default_library, self.work, "", novelist_api=self.generate_mock_api()
)
overview = lane.overview_facets(self._db, featured)
assert isinstance(overview, Facets)
assert Facets.COLLECTION_FULL == overview.collection
assert Facets.AVAILABLE_ALL == overview.availability
assert Facets.ORDER_AUTHOR == overview.order
# Entry point was preserved.
assert AudiobooksEntryPoint == overview.entrypoint
def test_works_query(self):
# Prep an empty result.
mock_api = self.generate_mock_api()
# With an empty recommendation result, the lane is empty.
lane = RecommendationLane(self._default_library, self.work, '', novelist_api=mock_api)
eq_(None, lane.works(self._db))
# Resulting recommendations are returned when available, though.
# TODO: Setting a data source name is necessary because Gutenberg
# books get filtered out when children or ya is one of the lane's
# audiences.
result = self._work(with_license_pool=True, data_source_name=DataSource.OVERDRIVE)
lane.recommendations = [result.license_pools[0].identifier]
SessionManager.refresh_materialized_views(self._db)
self.assert_works_queries(lane, [result])
def test_works_query(self):
# Prep an empty result.
mock_api = self.generate_mock_api()
# With an empty recommendation result, the lane is empty.
lane = RecommendationLane(self._default_library, self.work, '', novelist_api=mock_api)
eq_(None, lane.works(self._db))
# Resulting recommendations are returned when available, though.
# TODO: Setting a data source name is necessary because Gutenberg
# books get filtered out when children or ya is one of the lane's
# audiences.
result = self._work(with_license_pool=True, data_source_name=DataSource.OVERDRIVE)
lane.recommendations = [result.license_pools[0].identifier]
SessionManager.refresh_materialized_views(self._db)
self.assert_works_queries(lane, [result])
def test_works_query_with_source_language(self):
# Prepare a number of works with different languages.
# TODO: Setting a data source name is necessary because
# Gutenberg books get filtered out when children or ya
# is one of the lane's audiences.
eng = self._work(with_license_pool=True, language='eng', data_source_name=DataSource.OVERDRIVE)
fre = self._work(with_license_pool=True, language='fre', data_source_name=DataSource.OVERDRIVE)
spa = self._work(with_license_pool=True, language='spa', data_source_name=DataSource.OVERDRIVE)
SessionManager.refresh_materialized_views(self._db)
# They're all returned as recommendations from NoveList Select.
recommendations = list()
for work in [eng, fre, spa]:
recommendations.append(work.license_pools[0].identifier)
# But only the work that matches the source work is included.
mock_api = self.generate_mock_api()
lane = RecommendationLane(self._default_library, self.work, '', novelist_api=mock_api)
lane.recommendations = recommendations
self.assert_works_queries(lane, [eng])
# It doesn't matter the language.
self.work.presentation_edition.language = 'fre'
SessionManager.refresh_materialized_views(self._db)
mock_api = self.generate_mock_api()
lane = RecommendationLane(self._default_library, self.work, '', novelist_api=mock_api)
lane.recommendations = recommendations
self.assert_works_queries(lane, [fre])
def test_works_query_with_source_language(self):
# Prepare a number of works with different languages.
eng = self._work(with_license_pool=True, language='eng')
fre = self._work(with_license_pool=True, language='fre')
spa = self._work(with_license_pool=True, language='spa')
SessionManager.refresh_materialized_views(self._db)
# They're all returned as recommendations from NoveList Select.
recommendations = list()
for work in [eng, fre, spa]:
recommendations.append(work.license_pools[0].identifier)
# But only the work that matches the source work is included.
mock_api = self.generate_mock_api()
lane = RecommendationLane(self._db, self.lp, '', novelist_api=mock_api)
lane.recommendations = recommendations
self.assert_works_queries(lane, [eng])
# It doesn't matter the language.
self.lp.presentation_edition.language = 'fre'
SessionManager.refresh_materialized_views(self._db)
mock_api = self.generate_mock_api()
lane = RecommendationLane(self._db, self.lp, '', novelist_api=mock_api)
lane.recommendations = recommendations
self.assert_works_queries(lane, [fre])