def test_confusion_matrix_correct_metrics():
from collections import Counter
evaluated = [
EvaluationResult(results=Counter({
('O', 'O'): 150,
('O', 'PERSON'): 30,
('O', 'COMPANY'): 30,
('PERSON', 'PERSON'): 40,
('COMPANY', 'COMPANY'): 40,
('PERSON', 'COMPANY'): 10,
('COMPANY', 'PERSON'): 10,
('PERSON', 'O'): 30,
('COMPANY', 'O'): 30
}),
model_errors=None,
text=None)
]
model = MockTokensModel(prediction=None,
entities_to_keep=['PERSON', 'COMPANY'])
scores = model.calculate_score(evaluated, beta=2.5)
assert scores.pii_precision == 0.625
assert scores.pii_recall == 0.625
assert scores.entity_recall_dict['PERSON'] == 0.5
assert scores.entity_precision_dict['PERSON'] == 0.5
assert scores.entity_recall_dict['COMPANY'] == 0.5
assert scores.entity_precision_dict['COMPANY'] == 0.5
def test_evaluate_sample_wrong_entities_to_keep_correct_statistics():
prediction = ["O", "O", "O", "U-ANIMAL"]
model = MockTokensModel(prediction=prediction,
entities_to_keep=['SPACESHIP'])
sample = InputSample(full_text="I am the walrus",
masked="I am the [ANIMAL]",
spans=None)
sample.tokens = ["I", "am", "the", "walrus"]
sample.tags = ["O", "O", "O", "U-ANIMAL"]
evaluated = model.evaluate_sample(sample)
assert evaluated.results[("O", "O")] == 4
def test_evaluate_same_entity_correct_statistics():
prediction = ["O", "U-ANIMAL", "O", "U-ANIMAL"]
model = MockTokensModel(prediction=prediction, entities_to_keep=['ANIMAL'])
sample = InputSample(full_text="I dog the walrus",
masked="I [ANIMAL] the [ANIMAL]",
spans=None)
sample.tokens = ["I", "am", "the", "walrus"]
sample.tags = ["O", "O", "O", "U-ANIMAL"]
evaluation_result = model.evaluate_sample(sample)
assert evaluation_result.results[("O", "O")] == 2
assert evaluation_result.results[("ANIMAL", "ANIMAL")] == 1
assert evaluation_result.results[("O", "ANIMAL")] == 1
def test_confusion_matrix_2_correct_metrics():
from collections import Counter
evaluated = [
EvaluationResult(results=Counter({
('O', 'O'): 65467,
('O', 'ORG'): 4189,
('GPE', 'O'): 3370,
('PERSON', 'PERSON'): 2024,
('GPE', 'PERSON'): 1488,
('GPE', 'GPE'): 1033,
('O', 'GPE'): 964,
('ORG', 'ORG'): 914,
('O', 'PERSON'): 834,
('GPE', 'ORG'): 401,
('PERSON', 'ORG'): 35,
('PERSON', 'O'): 33,
('ORG', 'O'): 8,
('PERSON', 'GPE'): 5,
('ORG', 'PERSON'): 1
}),
model_errors=None,
text=None)
]
model = MockTokensModel(prediction=None)
scores = model.calculate_score(evaluated, beta=2.5)
pii_tp = evaluated[0].results[('PERSON', 'PERSON')] + \
evaluated[0].results[('ORG', 'ORG')] + \
evaluated[0].results[('GPE', 'GPE')] + \
evaluated[0].results[('ORG', 'GPE')] + \
evaluated[0].results[('ORG', 'PERSON')] + \
evaluated[0].results[('GPE', 'ORG')] + \
evaluated[0].results[('GPE', 'PERSON')] + \
evaluated[0].results[('PERSON', 'GPE')] + \
evaluated[0].results[('PERSON', 'ORG')]
pii_fp = evaluated[0].results[('O', 'PERSON')] + \
evaluated[0].results[('O', 'GPE')] + \
evaluated[0].results[('O', 'ORG')]
pii_fn = evaluated[0].results[('PERSON', 'O')] + \
evaluated[0].results[('GPE', 'O')] + \
evaluated[0].results[('ORG', 'O')]
assert scores.pii_precision == pii_tp / (pii_tp + pii_fp)
assert scores.pii_recall == pii_tp / (pii_tp + pii_fn)
def test_evaluate_multiple_tokens_partial_match_correct_statistics():
prediction = ["O", "O", "O", "B-ANIMAL", "L-ANIMAL", "O"]
model = MockTokensModel(prediction=prediction, entities_to_keep=['ANIMAL'])
sample = InputSample("I am the walrus amaericanus magnifico",
masked=None,
spans=None)
sample.tokens = ["I", "am", "the", "walrus", "americanus", "magnifico"]
sample.tags = ["O", "O", "O", "B-ANIMAL", "I-ANIMAL", "L-ANIMAL"]
evaluated = model.evaluate_sample(sample)
evaluation = model.calculate_score([evaluated])
assert evaluation.pii_precision == 1
assert evaluation.pii_recall == 4 / 6
def test_evaluator_simple():
prediction = ["O", "O", "O", "U-ANIMAL"]
model = MockTokensModel(prediction=prediction, entities_to_keep=['ANIMAL'])
sample = InputSample(full_text="I am the walrus",
masked="I am the [ANIMAL]",
spans=None)
sample.tokens = ["I", "am", "the", "walrus"]
sample.tags = ["O", "O", "O", "U-ANIMAL"]
evaluated = model.evaluate_sample(sample)
final_evaluation = model.calculate_score([evaluated])
assert final_evaluation.pii_precision == 1
assert final_evaluation.pii_recall == 1
def test_confusion_matrix_correct_metrics():
from collections import Counter
evaluated = [
EvaluationResult(
results=Counter({
("O", "O"): 150,
("O", "PERSON"): 30,
("O", "COMPANY"): 30,
("PERSON", "PERSON"): 40,
("COMPANY", "COMPANY"): 40,
("PERSON", "COMPANY"): 10,
("COMPANY", "PERSON"): 10,
("PERSON", "O"): 30,
("COMPANY", "O"): 30,
}),
model_errors=None,
text=None,
)
]
model = MockTokensModel(prediction=None)
evaluator = Evaluator(model=model, entities_to_keep=["PERSON", "COMPANY"])
scores = evaluator.calculate_score(evaluated, beta=2.5)
assert scores.pii_precision == 0.625
assert scores.pii_recall == 0.625
assert scores.entity_recall_dict["PERSON"] == 0.5
assert scores.entity_precision_dict["PERSON"] == 0.5
assert scores.entity_recall_dict["COMPANY"] == 0.5
assert scores.entity_precision_dict["COMPANY"] == 0.5
def test_evaluate_multiple_examples_ignore_entity_correct_statistics():
prediction = ["O", "O", "O", "U-PERSON", "O", "U-TENNIS_PLAYER"]
model = MockTokensModel(prediction=prediction,
labeling_scheme='BILOU',
entities_to_keep=['PERSON', 'TENNIS_PLAYER'])
input_sample = InputSample("My name is Raphael or David",
masked=None,
spans=None)
input_sample.tokens = ["My", "name", "is", "Raphael", "or", "David"]
input_sample.tags = ["O", "O", "O", "U-PERSON", "O", "U-PERSON"]
evaluated = model.evaluate_all(
[input_sample, input_sample, input_sample, input_sample])
scores = model.calculate_score(evaluated)
assert scores.pii_precision == 1
assert scores.pii_recall == 1
def test_confusion_matrix_2_correct_metrics():
from collections import Counter
evaluated = [
EvaluationResult(
results=Counter({
("O", "O"): 65467,
("O", "ORG"): 4189,
("GPE", "O"): 3370,
("PERSON", "PERSON"): 2024,
("GPE", "PERSON"): 1488,
("GPE", "GPE"): 1033,
("O", "GPE"): 964,
("ORG", "ORG"): 914,
("O", "PERSON"): 834,
("GPE", "ORG"): 401,
("PERSON", "ORG"): 35,
("PERSON", "O"): 33,
("ORG", "O"): 8,
("PERSON", "GPE"): 5,
("ORG", "PERSON"): 1,
}),
model_errors=None,
text=None,
)
]
model = MockTokensModel(prediction=None)
evaluator = Evaluator(model=model)
scores = evaluator.calculate_score(evaluated, beta=2.5)
pii_tp = (evaluated[0].results[("PERSON", "PERSON")] +
evaluated[0].results[("ORG", "ORG")] +
evaluated[0].results[("GPE", "GPE")] +
evaluated[0].results[("ORG", "GPE")] +
evaluated[0].results[("ORG", "PERSON")] +
evaluated[0].results[("GPE", "ORG")] +
evaluated[0].results[("GPE", "PERSON")] +
evaluated[0].results[("PERSON", "GPE")] +
evaluated[0].results[("PERSON", "ORG")])
pii_fp = (evaluated[0].results[("O", "PERSON")] +
evaluated[0].results[("O", "GPE")] +
evaluated[0].results[("O", "ORG")])
pii_fn = (evaluated[0].results[("PERSON", "O")] +
evaluated[0].results[("GPE", "O")] +
evaluated[0].results[("ORG", "O")])
assert scores.pii_precision == pii_tp / (pii_tp + pii_fp)
assert scores.pii_recall == pii_tp / (pii_tp + pii_fn)
def test_evaluate_multiple_examples_correct_statistics():
prediction = ["U-PERSON", "O", "O", "U-PERSON", "O", "O"]
model = MockTokensModel(prediction=prediction)
evaluator = Evaluator(model=model, entities_to_keep=["PERSON"])
input_sample = InputSample("My name is Raphael or David",
masked=None,
spans=None)
input_sample.tokens = ["My", "name", "is", "Raphael", "or", "David"]
input_sample.tags = ["O", "O", "O", "U-PERSON", "O", "U-PERSON"]
evaluated = evaluator.evaluate_all(
[input_sample, input_sample, input_sample, input_sample])
scores = evaluator.calculate_score(evaluated)
assert scores.pii_precision == 0.5
assert scores.pii_recall == 0.5
def test_evaluate_multiple_tokens_no_match_match_correct_statistics():
prediction = ["O", "O", "O", "B-SPACESHIP", "L-SPACESHIP", "O"]
model = MockTokensModel(prediction=prediction)
evaluator = Evaluator(model=model, entities_to_keep=["ANIMAL"])
sample = InputSample("I am the walrus amaericanus magnifico",
masked=None,
spans=None)
sample.tokens = ["I", "am", "the", "walrus", "americanus", "magnifico"]
sample.tags = ["O", "O", "O", "B-ANIMAL", "I-ANIMAL", "L-ANIMAL"]
evaluated = evaluator.evaluate_sample(sample, prediction)
evaluation = evaluator.calculate_score([evaluated])
assert np.isnan(evaluation.pii_precision)
assert evaluation.pii_recall == 0
def scores():
results = Counter(
{
("O", "O"): 30,
("ANIMAL", "ANIMAL"): 4,
("ANIMAL", "O"): 2,
("O", "ANIMAL"): 1,
("PERSON", "PERSON"): 2,
}
)
model = MockTokensModel(prediction=None)
evaluator = Evaluator(model=model)
evaluation_result = EvaluationResult(results=results)
return evaluator.calculate_score([evaluation_result])
def test_evaluate_multiple_entities_to_keep_correct_statistics():
prediction = ["O", "U-ANIMAL", "O", "U-ANIMAL"]
entities_to_keep = ["ANIMAL", "PLANT", "SPACESHIP"]
model = MockTokensModel(prediction=prediction)
evaluator = Evaluator(model=model, entities_to_keep=entities_to_keep)
sample = InputSample(full_text="I dog the walrus",
masked="I [ANIMAL] the [ANIMAL]",
spans=None)
sample.tokens = ["I", "am", "the", "walrus"]
sample.tags = ["O", "O", "O", "U-ANIMAL"]
evaluation_result = evaluator.evaluate_sample(sample, prediction)
assert evaluation_result.results[("O", "O")] == 2
assert evaluation_result.results[("ANIMAL", "ANIMAL")] == 1
assert evaluation_result.results[("O", "ANIMAL")] == 1
