def test_pandas_confusion_binary_cm_inverse():
y_true = [True, True, False, False, False, True, False, True, True,
False, True, False, False, False, False, False, True, False,
True, True, True, True, False, False, False, True, False,
True, False, False, False, False, True, True, False, False,
False, True, True, True, True, False, False, False, False,
True, False, False, False, False, False, False, False, False,
False, True, True, False, True, False, True, True, True,
False, False, True, False, True, False, False, True, False,
False, False, False, False, False, False, False, True, False,
True, True, True, True, False, False, True, False, True,
True, False, True, False, True, False, False, True, True,
False, False, True, True, False, False, False, False, False,
False, True, True, False]
y_pred = [False, False, False, False, False, True, False, False, True,
False, True, False, False, False, False, False, False, False,
True, True, True, True, False, False, False, False, False,
False, False, False, False, False, True, False, False, False,
False, True, False, False, False, False, False, False, False,
True, False, False, False, False, False, False, False, False,
False, True, False, False, False, False, False, False, False,
False, False, True, False, False, False, False, True, False,
False, False, False, False, False, False, False, True, False,
False, True, False, False, False, False, True, False, True,
True, False, False, False, True, False, False, True, True,
False, False, True, True, False, False, False, False, False,
False, True, False, False]
binary_cm = BinaryConfusionMatrix(y_true, y_pred)
bcm_sum = binary_cm.sum()
binary_cm_r = binary_cm.inverse() # reverse not in place
assert bcm_sum == binary_cm_r.sum()
def main(save, show):
basepath = os.path.dirname(__file__)
y_true = np.array([True, True, False, False, False, True, False, True, True,
False, True, False, False, False, False, False, True, False,
True, True, True, True, False, False, False, True, False,
True, False, False, False, False, True, True, False, False,
False, True, True, True, True, False, False, False, False,
True, False, False, False, False, False, False, False, False,
False, True, True, False, True, False, True, True, True,
False, False, True, False, True, False, False, True, False,
False, False, False, False, False, False, False, True, False,
True, True, True, True, False, False, True, False, True,
True, False, True, False, True, False, False, True, True,
False, False, True, True, False, False, False, False, False,
False, True, True, False])
y_pred = np.array([False, False, False, False, False, True, False, False, True,
False, True, False, False, False, False, False, False, False,
True, True, True, True, False, False, False, False, False,
False, False, False, False, False, True, False, False, False,
False, True, False, False, False, False, False, False, False,
True, False, False, False, False, False, False, False, False,
False, True, False, False, False, False, False, False, False,
False, False, True, False, False, False, False, True, False,
False, False, False, False, False, False, False, True, False,
False, True, False, False, False, False, True, False, True,
True, False, False, False, True, False, False, True, True,
False, False, True, True, False, False, False, False, False,
False, True, False, False])
# y_true = ~y_true
# y_pred = ~y_pred
binary_cm = BinaryConfusionMatrix(y_true, y_pred)
print("Binary confusion matrix:\n%s" % binary_cm)
print("")
# attributes = ['TP', 'TN', 'FP', 'FN', 'TPR', 'TNR', 'PPV', 'NPV', 'FPR', 'FDR',
# 'FNR', 'ACC', 'F1_score', 'MCC', 'informedness', 'markedness']
# binary_cm.print_stats(attributes)
binary_cm.print_stats()
# stats = binary_cm.stats(attributes)
# for key, val in stats.items():
# print("%s: %f" % (key, val))
print("sklearn confusion_matrix:\n%s" % confusion_matrix(y_true, y_pred))
f1score = f1_score(y_true, y_pred)
print("f1_score: %f" % f1score)
print("sklearn confusion_matrix_of_rev:\n%s" % confusion_matrix(~y_true, ~y_pred))
f1score_r = f1_score(~y_true, ~y_pred)
print("f1_score_of_rev: %f" % f1score_r)
print(classification_report(y_true, y_pred))
np.testing.assert_almost_equal(binary_cm.F1_score, f1score)
binary_cm.plot()
filename = 'binary_cm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
binary_cm.plot(normalized=True)
filename = 'binary_cm_norm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
# import seaborn as sns
# binary_cm.plot(normalized=True, backend=Backend.Seaborn)
# sns.plt.show()
print("FP+TP= %f" % (binary_cm.FP + binary_cm.TP)) # Positive
print("")
binary_cm_r = binary_cm.inverse()
print("Reversed binary confusion matrix:\n%s" % binary_cm_r)
binary_cm_r.print_stats()
np.testing.assert_almost_equal(binary_cm_r.F1_score, f1score_r)
print(binary_cm.classification_report)
print("sklearn confusion_matrix:\n%s" % confusion_matrix(y_true, y_pred))
print(classification_report(y_true, y_pred))
y_true = np.array(["a", "a", "b", "b", "b", "a", "b", "a", "a",
"b", "a", "b", "b", "b", "b", "b", "a", "b",
"a", "a", "a", "a", "b", "b", "b", "a", "b",
"a", "b", "b", "b", "b", "a", "a", "b", "b",
"b", "a", "a", "a", "a", "b", "b", "b", "b",
"a", "b", "b", "b", "b", "b", "b", "b", "b",
"b", "a", "a", "b", "a", "b", "a", "a", "a",
"b", "b", "a", "b", "a", "b", "b", "a", "b",
"b", "b", "b", "b", "b", "b", "b", "a", "b",
"a", "a", "a", "a", "b", "b", "a", "b", "a",
"a", "b", "a", "b", "a", "b", "b", "a", "a",
"b", "b", "a", "a", "b", "b", "b", "b", "b",
"b", "a", "a", "b"])
y_pred = np.array(["b", "b", "b", "b", "b", "a", "b", "b", "a",
"b", "a", "b", "b", "b", "b", "b", "b", "b",
"a", "a", "a", "a", "b", "b", "b", "b", "b",
"b", "b", "b", "b", "b", "a", "b", "b", "b",
"b", "a", "b", "b", "b", "b", "b", "b", "b",
"a", "b", "b", "b", "b", "b", "b", "b", "b",
"b", "a", "b", "b", "b", "b", "b", "b", "b",
"b", "b", "a", "b", "b", "b", "b", "a", "b",
"b", "b", "b", "b", "b", "b", "b", "a", "b",
"b", "a", "b", "b", "b", "b", "a", "b", "a",
"a", "b", "b", "b", "a", "b", "b", "a", "a",
"b", "b", "a", "a", "b", "b", "b", "b", "b",
"b", "a", "b", "b"])
binary_cm = BinaryConfusionMatrix(y_true, y_pred)
# binary_cm = BinaryConfusionMatrix(y_true, y_pred, labels=["a", "b"])
# binary_cm = BinaryConfusionMatrix(y_true, y_pred, labels=["b", "a"])
print(binary_cm)
print("")
binary_cm.print_stats()
print(binary_cm.classification_report)
print("sklearn confusion_matrix with string as input:\n%s" % confusion_matrix(y_true, y_pred))
print(classification_report(y_true, y_pred))
# ToFix
# "b" is considered as True
# "a" is considered as False
# but it should be "a" as True and "b" as False
# d = binary_cm.dict_class()
# print(d)
y_true_bool = binary_cm.y_true(to_bool=True)
y_pred_bool = binary_cm.y_pred(to_bool=True)
f1score = f1_score(y_true_bool, y_pred_bool)
np.testing.assert_almost_equal(binary_cm.F1_score, f1score)
print("F1_score: %f" % f1score)
def main(save, show):
basepath = os.path.dirname(__file__)
y_true = np.array([
True, True, False, False, False, True, False, True, True, False, True,
False, False, False, False, False, True, False, True, True, True, True,
False, False, False, True, False, True, False, False, False, False,
True, True, False, False, False, True, True, True, True, False, False,
False, False, True, False, False, False, False, False, False, False,
False, False, True, True, False, True, False, True, True, True, False,
False, True, False, True, False, False, True, False, False, False,
False, False, False, False, False, True, False, True, True, True, True,
False, False, True, False, True, True, False, True, False, True, False,
False, True, True, False, False, True, True, False, False, False,
False, False, False, True, True, False
])
y_pred = np.array([
False, False, False, False, False, True, False, False, True, False,
True, False, False, False, False, False, False, False, True, True,
True, True, False, False, False, False, False, False, False, False,
False, False, True, False, False, False, False, True, False, False,
False, False, False, False, False, True, False, False, False, False,
False, False, False, False, False, True, False, False, False, False,
False, False, False, False, False, True, False, False, False, False,
True, False, False, False, False, False, False, False, False, True,
False, False, True, False, False, False, False, True, False, True,
True, False, False, False, True, False, False, True, True, False,
False, True, True, False, False, False, False, False, False, True,
False, False
])
# y_true = ~y_true
# y_pred = ~y_pred
binary_cm = BinaryConfusionMatrix(y_true, y_pred)
print("Binary confusion matrix:\n%s" % binary_cm)
print("")
# attributes = ['TP', 'TN', 'FP', 'FN', 'TPR', 'TNR', 'PPV', 'NPV', 'FPR', 'FDR',
# 'FNR', 'ACC', 'F1_score', 'MCC', 'informedness', 'markedness']
# binary_cm.print_stats(attributes)
binary_cm.print_stats()
# stats = binary_cm.stats(attributes)
# for key, val in stats.items():
# print("%s: %f" % (key, val))
print("sklearn confusion_matrix:\n%s" % confusion_matrix(y_true, y_pred))
f1score = f1_score(y_true, y_pred)
print("f1_score: %f" % f1score)
print("sklearn confusion_matrix_of_rev:\n%s" %
confusion_matrix(~y_true, ~y_pred))
f1score_r = f1_score(~y_true, ~y_pred)
print("f1_score_of_rev: %f" % f1score_r)
print(classification_report(y_true, y_pred))
np.testing.assert_almost_equal(binary_cm.F1_score, f1score)
binary_cm.plot()
filename = 'binary_cm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
binary_cm.plot(normalized=True)
filename = 'binary_cm_norm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
# import seaborn as sns
# binary_cm.plot(normalized=True, backend=Backend.Seaborn)
# sns.plt.show()
print("FP+TP= %f" % (binary_cm.FP + binary_cm.TP)) # Positive
print("")
binary_cm_r = binary_cm.inverse()
print("Reversed binary confusion matrix:\n%s" % binary_cm_r)
binary_cm_r.print_stats()
np.testing.assert_almost_equal(binary_cm_r.F1_score, f1score_r)
print(binary_cm.classification_report)
print("sklearn confusion_matrix:\n%s" % confusion_matrix(y_true, y_pred))
print(classification_report(y_true, y_pred))
y_true = np.array([
"a", "a", "b", "b", "b", "a", "b", "a", "a", "b", "a", "b", "b", "b",
"b", "b", "a", "b", "a", "a", "a", "a", "b", "b", "b", "a", "b", "a",
"b", "b", "b", "b", "a", "a", "b", "b", "b", "a", "a", "a", "a", "b",
"b", "b", "b", "a", "b", "b", "b", "b", "b", "b", "b", "b", "b", "a",
"a", "b", "a", "b", "a", "a", "a", "b", "b", "a", "b", "a", "b", "b",
"a", "b", "b", "b", "b", "b", "b", "b", "b", "a", "b", "a", "a", "a",
"a", "b", "b", "a", "b", "a", "a", "b", "a", "b", "a", "b", "b", "a",
"a", "b", "b", "a", "a", "b", "b", "b", "b", "b", "b", "a", "a", "b"
])
y_pred = np.array([
"b", "b", "b", "b", "b", "a", "b", "b", "a", "b", "a", "b", "b", "b",
"b", "b", "b", "b", "a", "a", "a", "a", "b", "b", "b", "b", "b", "b",
"b", "b", "b", "b", "a", "b", "b", "b", "b", "a", "b", "b", "b", "b",
"b", "b", "b", "a", "b", "b", "b", "b", "b", "b", "b", "b", "b", "a",
"b", "b", "b", "b", "b", "b", "b", "b", "b", "a", "b", "b", "b", "b",
"a", "b", "b", "b", "b", "b", "b", "b", "b", "a", "b", "b", "a", "b",
"b", "b", "b", "a", "b", "a", "a", "b", "b", "b", "a", "b", "b", "a",
"a", "b", "b", "a", "a", "b", "b", "b", "b", "b", "b", "a", "b", "b"
])
binary_cm = BinaryConfusionMatrix(y_true, y_pred)
# binary_cm = BinaryConfusionMatrix(y_true, y_pred, labels=["a", "b"])
# binary_cm = BinaryConfusionMatrix(y_true, y_pred, labels=["b", "a"])
print(binary_cm)
print("")
binary_cm.print_stats()
print(binary_cm.classification_report)
print("sklearn confusion_matrix with string as input:\n%s" %
confusion_matrix(y_true, y_pred))
print(classification_report(y_true, y_pred))
# ToFix
# "b" is considered as True
# "a" is considered as False
# but it should be "a" as True and "b" as False
# d = binary_cm.dict_class()
# print(d)
y_true_bool = binary_cm.y_true(to_bool=True)
y_pred_bool = binary_cm.y_pred(to_bool=True)
f1score = f1_score(y_true_bool, y_pred_bool)
np.testing.assert_almost_equal(binary_cm.F1_score, f1score)
print("F1_score: %f" % f1score)
