def test_pandas_confusion_get(self):
y_true = ['rabbit', 'cat', 'rabbit', 'rabbit', 'cat', 'dog', 'dog', 'rabbit', 'rabbit', 'cat', 'dog', 'rabbit']
y_pred = ['cat', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'cat', 'rabbit', 'rabbit']
print("y_true: %s" % y_true)
print("y_pred: %s" % y_pred)
cm = ConfusionMatrix(y_true, y_pred)
assert isinstance(cm, pdml.confusion_matrix.LabeledConfusionMatrix)
assert cm.get("cat") == cm.get("cat", "cat")
assert cm.get("cat") == 3
assert cm.get("dog") == 1
assert cm.get("rabbit") == 3
assert cm.get("dog", "rabbit") == 2
def test_pandas_confusion_get(self):
y_true = ['rabbit', 'cat', 'rabbit', 'rabbit', 'cat', 'dog', 'dog', 'rabbit', 'rabbit', 'cat', 'dog', 'rabbit']
y_pred = ['cat', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'cat', 'rabbit', 'rabbit']
print("y_true: %s" % y_true)
print("y_pred: %s" % y_pred)
cm = ConfusionMatrix(y_true, y_pred)
assert isinstance(cm, pdml.confusion_matrix.LabeledConfusionMatrix)
assert cm.get("cat") == cm.get("cat", "cat")
assert cm.get("cat") == 3
assert cm.get("dog") == 1
assert cm.get("rabbit") == 3
assert cm.get("dog", "rabbit") == 2
def test_pandas_confusion_cm_stats_animals(self):
y_true = ['rabbit', 'cat', 'rabbit', 'rabbit', 'cat', 'dog', 'dog', 'rabbit', 'rabbit', 'cat', 'dog', 'rabbit']
y_pred = ['cat', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'cat', 'rabbit', 'rabbit']
print("y_true: %s" % y_true)
print("y_pred: %s" % y_pred)
cm = ConfusionMatrix(y_true, y_pred)
assert isinstance(cm, pdml.confusion_matrix.LabeledConfusionMatrix)
assert isinstance(cm.stats(), OrderedDict)
assert cm.population == len(y_true) # 12
cm.print_stats()
cm_stats = cm.stats() # noqa
assert cm.binarize("cat").TP == cm.get("cat") # cm.get("cat", "cat")
assert cm.binarize("cat").TP == 3
assert cm.binarize("dog").TP == cm.get("dog") # 1
assert cm.binarize("rabbit").TP == cm.get("rabbit") # 3
def test_pandas_confusion_cm_stats_animals(self):
y_true = ['rabbit', 'cat', 'rabbit', 'rabbit', 'cat', 'dog', 'dog', 'rabbit', 'rabbit', 'cat', 'dog', 'rabbit']
y_pred = ['cat', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'cat', 'rabbit', 'rabbit']
print("y_true: %s" % y_true)
print("y_pred: %s" % y_pred)
cm = ConfusionMatrix(y_true, y_pred)
assert isinstance(cm, pdml.confusion_matrix.LabeledConfusionMatrix)
assert isinstance(cm.stats(), OrderedDict)
assert cm.population == len(y_true) # 12
cm.print_stats()
cm_stats = cm.stats() # noqa
assert cm.binarize("cat").TP == cm.get("cat") # cm.get("cat", "cat")
assert cm.binarize("cat").TP == 3
assert cm.binarize("dog").TP == cm.get("dog") # 1
assert cm.binarize("rabbit").TP == cm.get("rabbit") # 3
def print_confusion_matrix(gold_id_labels, sys_id_labels):
gold_ids = gold_id_labels.keys()
gold_labels = [gold_id_labels[i] for i in gold_ids]
sys_labels = [sys_id_labels.get(i, 'unknown') for i in gold_ids]
c = ConfusionMatrix(gold_labels, sys_labels)
print('Confusion matrix:\n{0}'.format(c))
true_positives = c.get('yes', 'yes') + c.get('no', 'no')
true_negatives = c.get('unknown', 'unknown')
false_positives = c.get('unknown', 'yes') + c.get('unknown', 'no') + c.get(
'no', 'yes') + c.get('yes', 'no')
false_negatives = c.get('yes', 'unknown') + c.get('no', 'unknown')
print('Precision : {0:.4f}'.format(
float(true_positives) / (true_positives + false_positives)))
print('Recall : {0:.4f}'.format(
float(true_positives) / (true_positives + false_negatives)))
print('True positives : {0}'.format(true_positives))
print('True negatives : {0}'.format(true_negatives))
print('False positives: {0}'.format(false_positives))
print('False negatives: {0}'.format(false_negatives))
