def evaluate(
self,
test_set,
classifier=None,
accuracy=True,
f_measure=True,
precision=True,
recall=True,
verbose=False,
):
"""
Evaluate and print classifier performance on the test set.
:param test_set: A list of (tokens, label) tuples to use as gold set.
:param classifier: a classifier instance (previously trained).
:param accuracy: if `True`, evaluate classifier accuracy.
:param f_measure: if `True`, evaluate classifier f_measure.
:param precision: if `True`, evaluate classifier precision.
:param recall: if `True`, evaluate classifier recall.
:return: evaluation results.
:rtype: dict(str): float
"""
if classifier is None:
classifier = self.classifier
print("Evaluating {0} results...".format(type(classifier).__name__))
metrics_results = {}
if accuracy == True:
accuracy_score = eval_accuracy(classifier, test_set)
metrics_results["Accuracy"] = accuracy_score
gold_results = defaultdict(set)
test_results = defaultdict(set)
labels = set()
for i, (feats, label) in enumerate(test_set):
labels.add(label)
gold_results[label].add(i)
observed = classifier.classify(feats)
test_results[observed].add(i)
for label in labels:
if precision == True:
precision_score = eval_precision(
gold_results[label], test_results[label]
)
metrics_results["Precision [{0}]".format(label)] = precision_score
if recall == True:
recall_score = eval_recall(gold_results[label], test_results[label])
metrics_results["Recall [{0}]".format(label)] = recall_score
if f_measure == True:
f_measure_score = eval_f_measure(
gold_results[label], test_results[label]
)
metrics_results["F-measure [{0}]".format(label)] = f_measure_score
# Print evaluation results (in alphabetical order)
if verbose == True:
for result in sorted(metrics_results):
print("{0}: {1}".format(result, metrics_results[result]))
return metrics_results
def evaluate(
self,
test_set,
classifier=None,
accuracy=True,
f_measure=True,
precision=True,
recall=True,
verbose=False,
):
"""
Evaluate and print classifier performance on the test set.
:param test_set: A list of (tokens, label) tuples to use as gold set.
:param classifier: a classifier instance (previously trained).
:param accuracy: if `True`, evaluate classifier accuracy.
:param f_measure: if `True`, evaluate classifier f_measure.
:param precision: if `True`, evaluate classifier precision.
:param recall: if `True`, evaluate classifier recall.
:return: evaluation results.
:rtype: dict(str): float
"""
if classifier is None:
classifier = self.classifier
print("Evaluating {0} results...".format(type(classifier).__name__))
metrics_results = {}
if accuracy == True:
accuracy_score = eval_accuracy(classifier, test_set)
metrics_results['Accuracy'] = accuracy_score
gold_results = defaultdict(set)
test_results = defaultdict(set)
labels = set()
for i, (feats, label) in enumerate(test_set):
labels.add(label)
gold_results[label].add(i)
observed = classifier.classify(feats)
test_results[observed].add(i)
for label in labels:
if precision == True:
precision_score = eval_precision(
gold_results[label], test_results[label]
)
metrics_results['Precision [{0}]'.format(label)] = precision_score
if recall == True:
recall_score = eval_recall(gold_results[label], test_results[label])
metrics_results['Recall [{0}]'.format(label)] = recall_score
if f_measure == True:
f_measure_score = eval_f_measure(
gold_results[label], test_results[label]
)
metrics_results['F-measure [{0}]'.format(label)] = f_measure_score
# Print evaluation results (in alphabetical order)
if verbose == True:
for result in sorted(metrics_results):
print('{0}: {1}'.format(result, metrics_results[result]))
return metrics_results