def testAccumulatedResultsAdd(self):
result1 = eval_lib.IndividualResult()
result1.stats.true_positives = 30
result1.stats.false_positives = 20
result1.stats.false_negatives = 10
result1.per_type['TypeA'].true_positives = 9
result1.per_type['TypeA'].false_positives = 8
result1.per_type['TypeA'].false_negatives = 7
result1.per_type['TypeB'].true_positives = 6
result1.per_type['TypeB'].false_positives = 5
result1.per_type['TypeB'].false_negatives = 4
result1.typeless.true_positives = 15
result1.typeless.false_positives = 14
result1.typeless.false_negatives = 13
eval_lib.calculate_stats(result1.stats)
eval_lib.calculate_stats(result1.typeless)
result2 = eval_lib.IndividualResult()
result2.stats.true_positives = 3
result2.stats.false_positives = 2
result2.stats.false_negatives = 1
result2.per_type['TypeA'].true_positives = 19
result2.per_type['TypeA'].false_positives = 18
result2.per_type['TypeA'].false_negatives = 17
result2.per_type['TypeB'].true_positives = 16
result2.per_type['TypeB'].false_positives = 15
result2.per_type['TypeB'].false_negatives = 14
result2.typeless.true_positives = 13
result2.typeless.false_positives = 12
result2.typeless.false_negatives = 11
eval_lib.calculate_stats(result2.stats)
eval_lib.calculate_stats(result2.typeless)
ar = eval_lib.AccumulatedResults()
ar.add_result(result1)
ar.add_result(result2)
ar1 = eval_lib.AccumulatedResults()
ar1.add_result(result1)
ar2 = eval_lib.AccumulatedResults()
ar2.add_result(result2)
ar_sum = ar1 + ar2
self.assertEqual(ar.micro, ar_sum.micro)
self.assertEqual(ar.macro.calculate_stats(),
ar_sum.macro.calculate_stats())
self.assertEqual(ar.per_type, ar_sum.per_type)
self.assertEqual(ar.typeless_micro, ar_sum.typeless_micro)
self.assertEqual(ar.typeless_macro.calculate_stats(),
ar_sum.typeless_macro.calculate_stats())
def testPickle(self):
individual_result = eval_lib.IndividualResult()
individual_result.stats.true_positives = 30
individual_result.stats.false_positives = 20
individual_result.stats.false_negatives = 10
individual_result.per_type['TypeA'].true_positives = 9
individual_result.per_type['TypeA'].false_positives = 8
individual_result.per_type['TypeA'].false_negatives = 7
individual_result.per_type['TypeB'].true_positives = 6
individual_result.per_type['TypeB'].false_positives = 5
individual_result.per_type['TypeB'].false_negatives = 4
individual_result.typeless.true_positives = 15
individual_result.typeless.false_positives = 14
individual_result.typeless.false_negatives = 13
eval_lib.calculate_stats(individual_result.stats)
eval_lib.calculate_stats(individual_result.typeless)
pickled = pickle.dumps(individual_result)
unpickled = pickle.loads(pickled)
self.assertEqual(individual_result.record_id, unpickled.record_id)
self.assertEqual(individual_result.per_type, unpickled.per_type)
self.assertEqual(normalize_floats(individual_result.stats),
normalize_floats(unpickled.stats))
self.assertEqual(normalize_floats(individual_result.typeless),
normalize_floats(unpickled.typeless))
self.assertEqual(individual_result.debug_info, unpickled.debug_info)
ar = eval_lib.AccumulatedResults()
ar.add_result(individual_result)
pickled = pickle.dumps(ar)
unpickled = pickle.loads(pickled)
self.assertEqual(ar.micro, unpickled.micro)
self.assertEqual(ar.macro.calculate_stats(),
unpickled.macro.calculate_stats())
self.assertEqual(ar.per_type, unpickled.per_type)
self.assertEqual(ar.typeless_micro, unpickled.typeless_micro)
self.assertEqual(ar.typeless_macro.calculate_stats(),
unpickled.typeless_macro.calculate_stats())
def testAccumulateResults(self):
result1 = eval_lib.IndividualResult()
result1.stats.true_positives = 30
result1.stats.false_positives = 20
result1.stats.false_negatives = 10
result1.per_type['TypeA'].true_positives = 9
result1.per_type['TypeA'].false_positives = 8
result1.per_type['TypeA'].false_negatives = 7
result1.per_type['TypeB'].true_positives = 6
result1.per_type['TypeB'].false_positives = 5
result1.per_type['TypeB'].false_negatives = 4
result1.typeless.true_positives = 15
result1.typeless.false_positives = 14
result1.typeless.false_negatives = 13
eval_lib.calculate_stats(result1.stats)
eval_lib.calculate_stats(result1.typeless)
result2 = eval_lib.IndividualResult()
result2.stats.true_positives = 3
result2.stats.false_positives = 2
result2.stats.false_negatives = 1
result2.per_type['TypeA'].true_positives = 19
result2.per_type['TypeA'].false_positives = 18
result2.per_type['TypeA'].false_negatives = 17
result2.per_type['TypeB'].true_positives = 16
result2.per_type['TypeB'].false_positives = 15
result2.per_type['TypeB'].false_negatives = 14
result2.typeless.true_positives = 13
result2.typeless.false_positives = 12
result2.typeless.false_negatives = 11
eval_lib.calculate_stats(result2.stats)
eval_lib.calculate_stats(result2.typeless)
ar = eval_lib.AccumulatedResults()
ar.add_result(result1)
ar.add_result(result2)
expected_micro = results_pb2.Stats()
expected_micro.true_positives = 33
expected_micro.false_positives = 22
expected_micro.false_negatives = 11
self.assertEqual(expected_micro, ar.micro)
expected_macro = results_pb2.Stats()
expected_macro.precision = 0.6
expected_macro.recall = 0.75
expected_macro.f_score = 0.666667
self.assertEqual(normalize_floats(expected_macro),
normalize_floats(ar.macro.calculate_stats()))
expected_type_a = results_pb2.Stats()
expected_type_a.true_positives = 28
expected_type_a.false_positives = 26
expected_type_a.false_negatives = 24
expected_type_b = results_pb2.Stats()
expected_type_b.true_positives = 22
expected_type_b.false_positives = 20
expected_type_b.false_negatives = 18
expected_per_type = {
'TypeA': expected_type_a,
'TypeB': expected_type_b
}
self.assertEqual(expected_per_type, ar.per_type)
expected_typeless_micro = results_pb2.Stats()
expected_typeless_micro.true_positives = 28
expected_typeless_micro.false_positives = 26
expected_typeless_micro.false_negatives = 24
self.assertEqual(expected_typeless_micro, ar.typeless_micro)
expected_typeless_macro = results_pb2.Stats()
expected_typeless_macro.precision = 0.518621
expected_typeless_macro.recall = 0.53869
expected_typeless_macro.f_score = 0.528465
self.assertEqual(normalize_floats(expected_typeless_macro),
normalize_floats(ar.typeless_macro.calculate_stats()))
