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.sortField = "tf"
query.limit = 16
# Go ahead and query for all since short list and can get expected calculation results for all
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", "score"]
expectedData = \
[ RowItemModel( [-2, 2.0/13], headers ),
RowItemModel( [-5, 2.0/13], headers ),
RowItemModel( [-6, 2.0/13], headers ),
RowItemModel( [-1, 1.0/13], headers ),
RowItemModel( [-3, 1.0/13], headers ),
RowItemModel( [-7, 1.0/13], headers ),
RowItemModel( [-8, 1.0/13], headers ),
RowItemModel( [-10,1.0/13], headers ),
RowItemModel( [-11,1.0/13], headers ),
RowItemModel( [-12,1.0/13], headers ),
RowItemModel( [-13,1.0/13], headers ),
RowItemModel( [-14,1.0/13], headers ),
RowItemModel( [-15,1.0/13], 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])
expectedData = \
[ RowItemModel( [-2, 2.0/13], headers ),
RowItemModel( [-5, 2.0/13], headers ),
RowItemModel( [-6, 2.0/13], headers ),
RowItemModel( [-1, 1.0/13], headers ),
RowItemModel( [-3, 1.0/13], headers ),
RowItemModel( [-7, 1.0/13], headers ),
RowItemModel( [-8, 1.0/13], headers ),
RowItemModel( [-10,1.0/13], headers ),
RowItemModel( [-11,1.0/13], headers ),
RowItemModel( [-12,1.0/13], headers ),
RowItemModel( [-13,1.0/13], headers ),
RowItemModel( [-14,1.0/13], headers ),
RowItemModel( [-15,1.0/13], 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])
expectedData = \
[ #RowItemModel( [-2, 2.0/13], headers ),
RowItemModel( [-5, 2.0/13], headers ),
RowItemModel( [-6, 2.0/13], headers ),
#RowItemModel( [-1, 1.0/13], headers ),
#RowItemModel( [-3, 1.0/13], headers ),
RowItemModel( [-7, 1.0/13], headers ),
RowItemModel( [-8, 1.0/13], headers ),
RowItemModel( [-10,1.0/13], headers ),
RowItemModel( [-11,1.0/13], headers ),
RowItemModel( [-12,1.0/13], headers ),
RowItemModel( [-13,1.0/13], headers ),
#RowItemModel( [-14,1.0/13], headers ),
#RowItemModel( [-15,1.0/13], 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, -10])
query.excludeCategoryIds = set([-1, -4, -5, -6])
expectedData = \
[ #RowItemModel( [-2, 2.0/13], headers ),
RowItemModel( [-5, 2.0/13], headers ),
#RowItemModel( [-6, 2.0/13], headers ),
#RowItemModel( [-1, 1.0/13], headers ),
#RowItemModel( [-3, 1.0/13], headers ),
RowItemModel( [-7, 1.0/13], headers ),
RowItemModel( [-8, 1.0/13], headers ),
#RowItemModel( [-10,1.0/13], headers ),
RowItemModel( [-11,1.0/13], headers ),
RowItemModel( [-12,1.0/13], headers ),
RowItemModel( [-13,1.0/13], headers ),
#RowItemModel( [-14,1.0/13], headers ),
#RowItemModel( [-15,1.0/13], 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()
expectedData = \
[ RowItemModel( [-6, (1.0/6)*(2.0/2)+(1.0/4)*(1.0/2)], headers ),
#RowItemModel( [-5, (1.0/6)*(2.0/2)+(1.0/4)*(1.0/2)], headers ),
#RowItemModel( [-2, (1.0/6)*(1.0/2)+(1.0/6)*(2.0/2)], headers ),
RowItemModel( [-3, (1.0/6)*(2.0/2)], headers ),
RowItemModel( [-7, (1.0/6)*(2.0/2)], headers ),
RowItemModel( [-8, (1.0/6)*(2.0/2)], headers ),
RowItemModel( [-14,(1.0/4)*(1.0/2)], headers ),
RowItemModel( [-15,(1.0/4)*(1.0/2)], headers ),
RowItemModel( [-1, (1.0/6)*(1.0/2)], headers ),
RowItemModel( [-10,(1.0/6)*(1.0/2)], headers ),
RowItemModel( [-11,(1.0/6)*(1.0/2)], headers ),
RowItemModel( [-12,(1.0/6)*(1.0/2)], headers ),
RowItemModel( [-13,(1.0/6)*(1.0/2)], 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])
expectedData = \
[ #RowItemModel( [-6, (1.0/6)*(2.0/2)+(1.0/4)*(1.0/2)], headers ),
#RowItemModel( [-5, (1.0/6)*(2.0/2)+(1.0/4)*(1.0/2)], headers ),
#RowItemModel( [-2, (1.0/6)*(1.0/2)+(1.0/6)*(2.0/2)], headers ),
RowItemModel( [-3, (1.0/6)*(2.0/2)], headers ),
#RowItemModel( [-7, (1.0/6)*(2.0/2)], headers ),
#RowItemModel( [-8, (1.0/6)*(2.0/2)], headers ),
#RowItemModel( [-14,(1.0/4)*(1.0/2)], headers ),
#RowItemModel( [-15,(1.0/4)*(1.0/2)], headers ),
RowItemModel( [-1, (1.0/6)*(1.0/2)], headers ),
RowItemModel( [-10,(1.0/6)*(1.0/2)], headers ),
RowItemModel( [-11,(1.0/6)*(1.0/2)], headers ),
RowItemModel( [-12,(1.0/6)*(1.0/2)], headers ),
RowItemModel( [-13,(1.0/6)*(1.0/2)], 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()
expectedData = \
[ RowItemModel( [-6, (1.0/6)*(2.0/2)+(1.0/4)*(1.0/2)], headers ),
#RowItemModel( [-5, (1.0/6)*(2.0/2)+(1.0/4)*(1.0/2)], headers ),
#RowItemModel( [-2, (1.0/6)*(1.0/2)+(1.0/6)*(2.0/2)], headers ),
#RowItemModel( [-3, (1.0/6)*(2.0/2)], headers ),
RowItemModel( [-7, (1.0/6)*(2.0/2)], headers ),
RowItemModel( [-8, (1.0/6)*(2.0/2)], headers ),
RowItemModel( [-14,(1.0/4)*(1.0/2)], headers ),
RowItemModel( [-15,(1.0/4)*(1.0/2)], headers ),
RowItemModel( [-1, (1.0/6)*(1.0/2)], headers ),
RowItemModel( [-10,(1.0/6)*(1.0/2)], headers ),
RowItemModel( [-11,(1.0/6)*(1.0/2)], headers ),
RowItemModel( [-12,(1.0/6)*(1.0/2)], headers ),
RowItemModel( [-13,(1.0/6)*(1.0/2)], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
log.debug("General query, sort by TF*IDF lift.")
query.queryItemIds = set([-2, -5, -100])
query.excludeItemIds = set()
query.excludeCategoryIds = set()
query.sortField = "lift"
expectedData = \
[ #RowItemModel( [-5, (13.0/2)*((1.0/6)*(2.0/2)+(1.0/4)*(1.0/2))], headers ),
#RowItemModel( [-2, (13.0/2)*((1.0/6)*(1.0/2)+(1.0/6)*(2.0/2))], headers ),
RowItemModel( [-3, (13.0/1)*((1.0/6)*(2.0/2))], headers ),
RowItemModel( [-7, (13.0/1)*((1.0/6)*(2.0/2))], headers ),
RowItemModel( [-8, (13.0/1)*((1.0/6)*(2.0/2))], headers ),
RowItemModel( [-6, (13.0/2)*((1.0/6)*(2.0/2)+(1.0/4)*(1.0/2))], headers ),
RowItemModel( [-14,(13.0/1)*((1.0/4)*(1.0/2))], headers ),
RowItemModel( [-15,(13.0/1)*((1.0/4)*(1.0/2))], headers ),
RowItemModel( [-1, (13.0/1)*((1.0/6)*(1.0/2))], headers ),
RowItemModel( [-10,(13.0/1)*((1.0/6)*(1.0/2))], headers ),
RowItemModel( [-11,(13.0/1)*((1.0/6)*(1.0/2))], headers ),
RowItemModel( [-12,(13.0/1)*((1.0/6)*(1.0/2))], headers ),
RowItemModel( [-13,(13.0/1)*((1.0/6)*(1.0/2))], headers ),
]
recommendedData = self.recommender(query)
self.assertEqualRecommendedData(expectedData, recommendedData, query)
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
recQuery.sortField = "PPV"
results = recommender(recQuery)
for result in results:
itemIds.add(result["clinical_item_id"])
print >> sys.stderr, admitDxId, len(itemIds)