def test_pandas_confusion_cm_stats_animals():
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.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_binarize():
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']
cm = ConfusionMatrix(y_true, y_pred)
print("Confusion matrix:\n%s" % cm)
select = ['cat', 'dog']
print("Binarize with %s" % select)
binary_cm = cm.binarize(select)
print("Binary confusion matrix:\n%s" % binary_cm)
assert cm.sum() == binary_cm.sum()
def main(save, show):
basepath = os.path.dirname(__file__)
# y_true = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
# y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
# cm = ConfusionMatrix(y_true, y_pred)
# cm = ConfusionMatrix(y_true, y_pred, labels=["ant", "bird", "cat"])
# y_true = [2, 0, 2, 2, 0, 1]
# y_pred = [0, 0, 2, 2, 0, 2]
# cm = ConfusionMatrix(y_true, y_pred)
# cm = ConfusionMatrix(y_true, y_pred, labels=["ant", "bird", "cat"])
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']
cm = ConfusionMatrix(y_true, y_pred)
# y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
# y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
# >>> cm(y_true, y_pred, labels=["ant", "bird", "cat"])
# array([[2, 0, 0],
# [0, 0, 1],
# [1, 0, 2]])
# cm = ConfusionMatrix(y_true, y_pred)
print("Confusion matrix:\n%s" % cm)
df = cm.to_dataframe()
print(df)
print(df.dtypes)
cm.plot()
filename = 'cm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
cm.plot(normalized=True)
filename = 'cm_norm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
cm.print_stats()
print(cm.classification_report)
print("sklearn confusion_matrix:\n%s" % confusion_matrix(y_true, y_pred))
print(classification_report(y_true, y_pred))
# stat = 'precision'
# print(cm._avg_stat(stat))
# print(cm.ACC)
# import seaborn as sns
# cm.plot(normalized=True, backend=Backend.Seaborn)
# sns.plt.show()
print("Binarize a confusion matrix")
y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
cm = ConfusionMatrix(y_true, y_pred)
print(cm)
binary_cm = cm.binarize(['ant', 'cat'])
# A bird is not a "land_animal"
print(binary_cm)
def main(save, show):
basepath = os.path.dirname(__file__)
# y_true = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
# y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
# cm = ConfusionMatrix(y_true, y_pred)
# cm = ConfusionMatrix(y_true, y_pred, labels=["ant", "bird", "cat"])
# y_true = [2, 0, 2, 2, 0, 1]
# y_pred = [0, 0, 2, 2, 0, 2]
# cm = ConfusionMatrix(y_true, y_pred)
# cm = ConfusionMatrix(y_true, y_pred, labels=["ant", "bird", "cat"])
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'
]
cm = ConfusionMatrix(y_true, y_pred)
# y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
# y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
# >>> cm(y_true, y_pred, labels=["ant", "bird", "cat"])
# array([[2, 0, 0],
# [0, 0, 1],
# [1, 0, 2]])
# cm = ConfusionMatrix(y_true, y_pred)
print("Confusion matrix:\n%s" % cm)
df = cm.to_dataframe()
print(df)
print(df.dtypes)
cm.plot()
filename = 'cm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
cm.plot(normalized=True)
filename = 'cm_norm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
cm.print_stats()
print(cm.classification_report)
print("sklearn confusion_matrix:\n%s" % confusion_matrix(y_true, y_pred))
print(classification_report(y_true, y_pred))
# stat = 'precision'
# print(cm._avg_stat(stat))
# print(cm.ACC)
# import seaborn as sns
# cm.plot(normalized=True, backend=Backend.Seaborn)
# sns.plt.show()
print("Binarize a confusion matrix")
y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
cm = ConfusionMatrix(y_true, y_pred)
print(cm)
binary_cm = cm.binarize(['ant', 'cat'])
# A bird is not a "land_animal"
print(binary_cm)
