def MAP(self,rank):
Map = MeanAveragePrecision()
for elem in rank:
Map.load(self.rating,elem)
result=Map.computr()
print "the MAP of ranking :" ,result
return result
def test_RANK_MeanAveragePrecision(self):
mavgp = MeanAveragePrecision()
GT_DECISION = [1, 2, 4]
TEST_DECISION = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
mavgp.load(GT_DECISION, TEST_DECISION)
GT_DECISION = [1, 4, 8]
mavgp.load(GT_DECISION, TEST_DECISION)
GT_DECISION = [3, 5, 9, 25, 39, 44, 56, 71, 89, 123]
TEST_DECISION = [
123, 84, 56, 6, 8, 9, 511, 129, 187, 25, 38, 48, 250, 113, 3
]
mavgp.load(GT_DECISION, TEST_DECISION)
assert_equal(mavgp.compute(), 0.707222)
def test_RANK_MeanAveragePrecision(self):
mavgp = MeanAveragePrecision()
GT_DECISION = [1, 2, 4]
TEST_DECISION = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
mavgp.load(GT_DECISION, TEST_DECISION)
GT_DECISION = [1, 4, 8]
mavgp.load(GT_DECISION, TEST_DECISION)
GT_DECISION = [3, 5, 9, 25, 39, 44, 56, 71, 89, 123]
TEST_DECISION = [123, 84, 56, 6, 8, 9, 511, 129, 187, 25, 38, 48, 250, 113, 3]
mavgp.load(GT_DECISION, TEST_DECISION)
assert_equal(mavgp.compute(), 0.707222)
n = 100
step = 2
start = 10
(prec, recall, F1) = 0.0, 0.0, 0.0
count = 0
precision = []
recall = []
roc_auc = dict()
total_recall = 0.0
total_mean_prec = 0.0
inner_count = 0
filename = "/home/dhiraj/projects/results/avg_precision_results_10-60.txt"
mavgp = MeanAveragePrecision()
with open(filename, "w") as myFile:
while count < n:
users = db.get_people_sorted_artists(start, step)
for user in users:
TEST_DECISION = []
GT_DECISION = []
s_matrix_vector = db.get_sparse_matrix_vector(str(user["_id"]).encode('utf-8'))
if s_matrix_vector and len(s_matrix_vector[0]['array']) > 0:
inner_count += 1
if inner_count < (step+1):
v_vectors = functions.compute_v_vectors(s_matrix_vector[0]['col_index'])
step = 0
start = 119
(prec, recall, F1) = 0.0, 0.0, 0.0
count = 0
precision = []
recall = []
roc_auc = dict()
total_recall = 0.0
total_prec = 0.0
total_f1 = 0.0
total_mean_prec = 0.0
inner_count = 0
filename = "/home/dhiraj/projects/results/dataset2/precision_results.txt"
mavgp = MeanAveragePrecision()
with open(filename, "w") as myFile:
#while count < n:
users = db.get_people_sorted_artists(start, step)
for user in users:
TEST_DECISION = []
GT_DECISION = []
s_matrix_vector = db.get_sparse_matrix_vector(str(user["_id"]).encode('utf-8'))
if s_matrix_vector and len(s_matrix_vector[0]['array']) > 0:
# inner_count += 1
# if inner_count < (step+1):
v_vectors = functions.compute_v_vectors(s_matrix_vector[0]['col_index'])
counts = dict()
for vec in s_matrix_vector[0]['array']:
