def testBinarySVMDefaultWeights(self):
target_column = target_column_lib.binary_svm_target()
predictions = constant_op.constant([[-0.5], [1.2]])
labels = constant_op.constant([0, 1])
loss = target_column.loss(predictions, labels, {})
# Prediction for first example is in the right side of the hyperplane (i.e.,
# < 0) but it is within the [-1,1] margin. There is a 0.5 loss incurred by
# this example. The 2nd prediction is outside the margin so it incurs no
# loss at all. The overall (normalized) loss is therefore 0.5/(1+1) = 0.25.
with session.Session() as sess:
self.assertAlmostEqual(0.25, sess.run(loss))
def testBinarySVMDefaultWeights(self):
target_column = target_column_lib.binary_svm_target()
predictions = constant_op.constant([[-0.5], [1.2]])
labels = constant_op.constant([0, 1])
loss = target_column.loss(predictions, labels, {})
# Prediction for first example is in the right side of the hyperplane (i.e.,
# < 0) but it is within the [-1,1] margin. There is a 0.5 loss incurred by
# this example. The 2nd prediction is outside the margin so it incurs no
# loss at all. The overall (normalized) loss is therefore 0.5/(1+1) = 0.25.
with session.Session() as sess:
self.assertAlmostEqual(0.25, sess.run(loss))
def testBinarySVMWithWeights(self):
target_column = target_column_lib.binary_svm_target(
weight_column_name="weights")
predictions = constant_op.constant([[-0.7], [0.2]])
labels = constant_op.constant([0, 1])
features = {"weights": constant_op.constant([2.0, 10.0])}
loss = target_column.loss(predictions, labels, features)
training_loss = target_column.training_loss(predictions, labels, features)
# Prediction for both examples are in the right side of the hyperplane but
# within the margin. The (weighted) loss incurred is 2*0.3=0.6 and 10*0.8=8
# respectively. The overall (normalized) loss is therefore 8.6/12.
with session.Session() as sess:
self.assertAlmostEqual(8.6 / 12, sess.run(loss), places=3)
self.assertAlmostEqual(8.6 / 2, sess.run(training_loss), places=3)
def testBinarySVMWithWeights(self):
target_column = target_column_lib.binary_svm_target(
weight_column_name="weights")
predictions = constant_op.constant([[-0.7], [0.2]])
labels = constant_op.constant([0, 1])
features = {"weights": constant_op.constant([2.0, 10.0])}
loss = target_column.loss(predictions, labels, features)
training_loss = target_column.training_loss(predictions, labels,
features)
# Prediction for both examples are in the right side of the hyperplane but
# within the margin. The (weighted) loss incurred is 2*0.3=0.6 and 10*0.8=8
# respectively. The overall (normalized) loss is therefore 8.6/12.
with session.Session() as sess:
self.assertAlmostEqual(8.6 / 12, sess.run(loss), places=3)
self.assertAlmostEqual(8.6 / 2, sess.run(training_loss), places=3)