def test_recommender(self):
# Run the recommender against the mock test data above and verify expected stats afterwards.
query = RecommenderQuery()
#query.queryItemIds = set();
#query.excludeItemIds = set();
#query.categoryIds = set();
#query.timeDeltaMax = None; # If set to one of the constants (DELTA_ZERO, DELTA_HOUR, etc.), will count item associations that occurred within that time delta as co-occurrent. If left blank, will just consider all items within a given patient as co-occurrent.
query.limit = 3
# Just get top 3 ranks for simplicity
query.maxRecommendedId = 0
# Artificial constraint to focus only on test data
log.debug(
"Query with no item key input, just return ranks by general likelihood then."
)
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-3], headers ),
RowItemModel( [-6], headers ),
RowItemModel( [-5], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug(
"Query with key item inputs for which no data exists. Effecitvely ignore it then, so just return ranks by general likelihood."
)
query.queryItemIds = set([-100])
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-3], headers ),
RowItemModel( [-6], headers ),
RowItemModel( [-5], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug("Query with category filter on recommended results.")
query.queryItemIds = set([-100])
query.excludeCategoryIds = set([-1, -4, -5, -6])
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-6], headers ),
RowItemModel( [-5], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug(
"Query with category filter and specific exclusion filter on recommended results."
)
query.queryItemIds = set([-100])
query.excludeItemIds = set([-6])
query.excludeCategoryIds = set([-1, -4, -5, -6])
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-5], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug(
"General query with a couple of input clinical items + one with no association data (should effectively be ignored)."
)
query.queryItemIds = set([-2, -5, -100])
query.excludeItemIds = set()
query.excludeCategoryIds = set()
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-4], headers ),
RowItemModel( [-6], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug(
"General query but set a limit on time delta worth counting item associations"
)
query.queryItemIds = set([-2, -5, -100])
query.excludeItemIds = set()
query.excludeCategoryIds = set()
query.timeDeltaMax = DELTA_HOUR
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-6], headers ),
RowItemModel( [-4], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug("General query with category limit")
query.queryItemIds = set([-2, -5, -100])
query.excludeItemIds = set()
query.excludeCategoryIds = set([-2, -4, -5, -6])
query.timeDeltaMax = DELTA_HOUR
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-4], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug("General query with specific exclusion")
query.queryItemIds = set([-2, -5, -100])
query.excludeItemIds = set([-4, -3, -2])
query.excludeCategoryIds = set()
query.timeDeltaMax = DELTA_HOUR
headers = ["clinical_item_id"]
expectedData = \
[ RowItemModel( [-6], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
if admitDxId not in itemIdsByAdmitDxId:
itemIdsByAdmitDxId[admitDxId] = set()
itemIdsByAdmitDxId[admitDxId].add(itemId)
admitDxIdSectionGuidelineNameTuples.add(
(admitDxId, sectionName, guidelineName))
recommender = ItemAssociationRecommender()
for admitDxId, itemIds in itemIdsByAdmitDxId.iteritems():
print >> sys.stderr, admitDxId, len(itemIds)
recQuery = RecommenderQuery()
recQuery.excludeItemIds = recommender.defaultExcludedClinicalItemIds()
recQuery.excludeCategoryIds = recommender.defaultExcludedClinicalItemCategoryIds(
)
recQuery.queryItemIds = [admitDxId]
recQuery.timeDeltaMax = timedelta(1)
# Within one day
recQuery.countPrefix = "patient_"
recQuery.limit = TOP_ITEM_COUNT
# Top results by P-value
recQuery.sortField = "P-YatesChi2-NegLog"
results = recommender(recQuery)
#recommender.formatRecommenderResults(results);
for result in results:
itemIds.add(result["clinical_item_id"])
#print >> sys.stderr, result["description"];
print >> sys.stderr, admitDxId, len(itemIds)
# Top results by PPV