def test_pandas_confusion_binary_cm_inverse(self):
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 = ConfusionMatrix(y_true, y_pred)
assert isinstance(binary_cm, pdml.confusion_matrix.BinaryConfusionMatrix)
bcm_sum = binary_cm.sum()
binary_cm_r = binary_cm.inverse() # reverse not in place
assert bcm_sum == binary_cm_r.sum()
def test_pandas_confusion_binary_cm_inverse(self):
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 = ConfusionMatrix(y_true, y_pred)
assert isinstance(binary_cm, pdml.confusion_matrix.BinaryConfusionMatrix)
bcm_sum = binary_cm.sum()
binary_cm_r = binary_cm.inverse() # reverse not in place
assert bcm_sum == binary_cm_r.sum()
def test_pandas_confusion_cm_binarize(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']
cm = ConfusionMatrix(y_true, y_pred)
assert isinstance(cm, pdml.confusion_matrix.LabeledConfusionMatrix)
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 test_pandas_confusion_cm_binarize(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']
cm = ConfusionMatrix(y_true, y_pred)
assert isinstance(cm, pdml.confusion_matrix.LabeledConfusionMatrix)
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()
test_error = classifier.score(X_test, Y_test)
#print(train_error, test_error)
#-------------> EVALUATION
evaluation_file.write('SVM_EVALUATION:' + title)
evaluation_file.write('\n\nConfusion_matrix:\n')
evaluation_file.write(str(confusion_matrix(Y_test, Y_pred)))
evaluation_file.write('\n\nClassification report:\n')
evaluation_file.write(str(classification_report(Y_test, Y_pred)))
#evaluation_file.write("\nAccuracy is "+str(accuracy_score(Y_test,Y_pred)*100) + '\n')
evaluation_file.write('\n\n')
cm = ConfusionMatrix(Y_test, Y_pred)
evaluation_file.write(str(cm.stats()))
cm = confusion_matrix(Y_test, Y_pred)
cm = cm.astype('float') * 100 / cm.sum(axis=1)[:, np.newaxis]
sn.heatmap(cm, annot=True, cmap='YlGnBu')
plt.xticks(np.arange(0.5, 4.5, 1), ('A', 'B', 'C', 'D'))
plt.yticks(np.arange(0.5, 4.5, 1), ('A', 'B', 'C', 'D'))
plt.savefig(path_results + title + '/' + 'Confusion_Matrix.pdf')
#plt.show()
plt.close()
print("\nAccuracy is " + str(accuracy_score(Y_test, Y_pred) * 100) + '\n')
#X_test_plot = scaling.inverse_transform(X_test)
value = 1.5
width = 100
scatter_kwargs = {'s': 0.001, 'edgecolor': None, 'alpha': 0.7}
