def test_from_data(self):
# with self.assertRaises(RuntimeError) as cm:
# i = np.atleast_2d(np.array([0, 1])).T
# t = np.atleast_2d(np.array([2])).T
# c = ConfusionMatrix.from_data(3, i, t)
# self.assertEqual(cm.exception.message, "Output and target data should have the same shape")
def func():
i = np.atleast_2d(np.array([0, 1])).T
t = np.atleast_2d(np.array([2])).T
c = ConfusionMatrix.from_data(3, i, t)
self.assertRaises(RuntimeError, func)
i = np.atleast_2d(np.array([0, 0, 1, 2, 0, 0, 2, 2, 1, 2])).T
t = np.atleast_2d(np.array([0, 0, 0, 1, 1, 0, 2, 2, 1, 1])).T
c = ConfusionMatrix.from_data(3, i, t)
self.assertTrue(np.all(c.truth == [4, 4, 2]))
self.assertTrue(np.all(c.detections == [4, 2, 4]))
self.assertTrue(np.all(c.correct == [3, 1, 2]))
self.assertTrue(np.all(c.incorrect == [1, 3, 0]))
def func():
i = np.atleast_2d(np.array([0, 1])).T
t = np.atleast_2d(np.array([2])).T
c = ConfusionMatrix.from_data(3, i, t)
# testout = flow(test_inputs[0])
# trainout = flow(train_inputs[0])
# testout = flow(test_inputs[0])
# print "shape of testout: ", numpy.shape(testout)
import scipy as sp
from Oger.utils import ConfusionMatrix, plot_conf
ytest = []
for xtest in test_inputs:
ytest.append(opt_flow(xtest))
ymean = sp.atleast_2d(sp.array([sp.argmax(mdp.numx.atleast_2d(mdp.numx.mean(sample, axis=0))) for sample in test_targets])).T
ytestmean = sp.atleast_2d(sp.array([sp.argmax(mdp.numx.atleast_2d(mdp.numx.mean(sample, axis=0))) for sample in ytest])).T
# use ConfusionMatrix to compute some more information about the
confusion_matrix = ConfusionMatrix.from_data(61, ytestmean , ymean) # 61 classes
print "Error rate: %.4f" % confusion_matrix.error_rate # this comes down to 0-1 loss
print "Balanced error rate: %.4f" % confusion_matrix.ber
print
# compute precision and recall for each class vs. all others
print "Per-class precision and recall"
binary_confusion_matrices = confusion_matrix.binary()
for c in range(61):
m = binary_confusion_matrices[c]
print "label %d - precision: %.2f, recall %.2f" % (c, m.precision, m.recall)
print
# properties of the ConfusionMatrix and BinaryConfusionMatrix classes can also be used
# as error measure functions, as follows:
ber = ConfusionMatrix.error_measure('ber', 61) # 61-class balanced error rate
for (sample, target) in zip(ytest, outputs[n_train_samples:])
]))
ymean = sp.atleast_2d(
sp.array([
sp.argmax(mdp.numx.atleast_2d(mdp.numx.mean(sample, axis=0)))
for sample in outputs[n_train_samples:]
])).T
ytestmean = sp.atleast_2d(
sp.array([
sp.argmax(mdp.numx.atleast_2d(mdp.numx.mean(sample, axis=0)))
for sample in ytest
])).T
# use ConfusionMatrix to compute some more information about the
confusion_matrix = ConfusionMatrix.from_data(10, ytestmean,
ymean) # 10 classes
print "Error rate: %.4f" % confusion_matrix.error_rate # this comes down to 0-1 loss
print "Balanced error rate: %.4f" % confusion_matrix.ber
print
# compute precision and recall for each class vs. all others
print "Per-class precision and recall"
binary_confusion_matrices = confusion_matrix.binary()
for c in range(10):
m = binary_confusion_matrices[c]
print "label %d - precision: %.2f, recall %.2f" % (c, m.precision,
m.recall)
print
# properties of the ConfusionMatrix and BinaryConfusionMatrix classes can also be used
# as error measure functions, as follows:
ytest.append(flow(xtest))
pylab.subplot(n_subplots_x, n_subplots_y, 2)
pylab.plot(reservoir.inspect()[0])
pylab.title("Sample reservoir states")
pylab.xlabel("Timestep")
pylab.ylabel("Activation")
print "Error : " + str(mdp.numx.mean([loss_01_time(sample, target) for (sample, target) in zip(ytest, outputs[n_train_samples:])]))
ymean = sp.array([sp.argmax(mdp.numx.atleast_2d(mdp.numx.mean(sample, axis=0))) for sample in
outputs[n_train_samples:]])
ytestmean = sp.array([sp.argmax(mdp.numx.atleast_2d(mdp.numx.mean(sample, axis=0))) for sample in ytest])
# use ConfusionMatrix to compute some more information about the
confusion_matrix = ConfusionMatrix.from_data(10, ytestmean, ymean) # 10 classes
print "Error rate: %.4f" % confusion_matrix.error_rate # this comes down to 0-1 loss
print "Balanced error rate: %.4f" % confusion_matrix.ber
print
# compute precision and recall for each class vs. all others
print "Per-class precision and recall"
binary_confusion_matrices = confusion_matrix.binary()
for c in range(10):
m = binary_confusion_matrices[c]
print "label %d - precision: %.2f, recall %.2f" % (c, m.precision, m.recall)
print
# properties of the ConfusionMatrix and BinaryConfusionMatrix classes can also be used
# as error measure functions, as follows:
ber = ConfusionMatrix.error_measure('ber', 10) # 10-class balanced error rate
ytest = []
for xtest in test_inputs:
ytest.append(opt_flow(xtest))
ymean = sp.atleast_2d(
sp.array([
sp.argmax(mdp.numx.atleast_2d(mdp.numx.mean(sample, axis=0)))
for sample in test_targets
])).T
ytestmean = sp.atleast_2d(
sp.array([
sp.argmax(mdp.numx.atleast_2d(mdp.numx.mean(sample, axis=0)))
for sample in ytest
])).T
# use ConfusionMatrix to compute some more information about the
confusion_matrix = ConfusionMatrix.from_data(61, ytestmean,
ymean) # 61 classes
print "Error rate: %.4f" % confusion_matrix.error_rate # this comes down to 0-1 loss
print "Balanced error rate: %.4f" % confusion_matrix.ber
print
# compute precision and recall for each class vs. all others
print "Per-class precision and recall"
binary_confusion_matrices = confusion_matrix.binary()
for c in range(61):
m = binary_confusion_matrices[c]
print "label %d - precision: %.2f, recall %.2f" % (c, m.precision,
m.recall)
print
# properties of the ConfusionMatrix and BinaryConfusionMatrix classes can also be used
# as error measure functions, as follows: