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from nose.tools import eq_, assert_less, assert_greater, assert_almost_equal
import numpy
numpy.random.seed(0)
import logging
logging.getLogger('tensorflow').disabled = True
import keras.backend as K
from mhcflurry.custom_loss import CUSTOM_LOSSES
y_true = numpy.array(y_true)
y_pred = numpy.array(y_pred)
if y_pred.ndim == 1:
y_pred = y_pred.reshape((len(y_pred), 1))
assert y_pred.ndim == 2
if K.backend() == "tensorflow":
session = K.get_session()
y_true_var = K.constant(y_true, name="y_true")
y_pred_var = K.constant(y_pred, name="y_pred")
result = loss(y_true_var, y_pred_var)
return result.eval(session=session)
elif K.backend() == "theano":
y_true_var = K.constant(y_true, name="y_true")
y_pred_var = K.constant(y_pred, name="y_pred")
result = loss(y_true_var, y_pred_var)
return result.eval()
else:
raise ValueError("Unsupported backend: %s" % K.backend())
def test_mse_with_inequalities(loss_obj=CUSTOM_LOSSES['mse_with_inequalities']):
y_values = [0.0, 0.5, 0.8, 1.0]
adjusted_y = loss_obj.encode_y(y_values)
print(adjusted_y)
loss0 = evaluate_loss(loss_obj.loss, adjusted_y, y_values)
print(loss0)
eq_(loss0, 0.0)
adjusted_y = loss_obj.encode_y(y_values, [">", ">", ">", ">"])
loss0 = evaluate_loss(loss_obj.loss, adjusted_y, y_values)
eq_(loss0, 0.0)
adjusted_y = loss_obj.encode_y(y_values, ["<", "<", "<", "<"])
loss0 = evaluate_loss(loss_obj.loss, adjusted_y, y_values)
eq_(loss0, 0.0)
adjusted_y = loss_obj.encode_y(y_values, ["=", "<", "=", ">"])
loss0 = evaluate_loss(loss_obj.loss, adjusted_y, y_values)
eq_(loss0, 0.0)
adjusted_y = loss_obj.encode_y(y_values, ["=", "<", "=", ">"])
loss0 = evaluate_loss(loss_obj.loss, adjusted_y, [0.0, 0.4, 0.8, 1.0])
eq_(loss0, 0.0)
adjusted_y = loss_obj.encode_y(y_values, [">", "<", ">", ">"])
loss0 = evaluate_loss(loss_obj.loss, adjusted_y, [0.1, 0.4, 0.9, 1.0])
eq_(loss0, 0.0)
adjusted_y = loss_obj.encode_y(y_values, [">", "<", ">", ">"])
loss0 = evaluate_loss(loss_obj.loss, adjusted_y, [0.1, 0.6, 0.9, 1.0])
assert_greater(loss0, 0.0)
adjusted_y = loss_obj.encode_y(y_values, ["=", "<", ">", ">"])
loss0 = evaluate_loss(loss_obj.loss, adjusted_y, [0.1, 0.6, 0.9, 1.0])
assert_almost_equal(loss0, 0.02 / 4)
adjusted_y = loss_obj.encode_y(y_values, ["=", "<", "=", ">"])
loss0 = evaluate_loss(loss_obj.loss, adjusted_y, [0.1, 0.6, 0.9, 1.0])
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assert_almost_equal(loss0, 0.03 / 4)
def test_mse_with_inequalities_and_multiple_outputs():
loss_obj = CUSTOM_LOSSES['mse_with_inequalities_and_multiple_outputs']
test_mse_with_inequalities(loss_obj)
y_values = [0.0, 0.5, 0.8, 1.0]
adjusted_y = loss_obj.encode_y(
y_values, output_indices=[0, 1, 1, 1])
loss0 = evaluate_loss(
loss_obj.loss,
adjusted_y,
[
[0.0, 1000],
[2000, 0.5],
[3000, 0.8],
[4000, 1.0],
])
assert_almost_equal(loss0, 0.0)
y_values = [0.0, 0.5, 0.8, 1.0]
adjusted_y = loss_obj.encode_y(
y_values, output_indices=[0, 1, 1, 0])
loss0 = evaluate_loss(
loss_obj.loss,
adjusted_y,
[
[0.1, 1000],
[2000, 0.6],
[3000, 0.8],
[1.0, 4000],
])
assert_almost_equal(loss0, 0.02 / 4)
y_values = [0.0, 0.5, 0.8, 1.0]
adjusted_y = loss_obj.encode_y(
y_values, output_indices=[0, 1, 1, 0], inequalities=["=", ">", "<", "<"])
loss0 = evaluate_loss(
loss_obj.loss,
adjusted_y,
[
[0.1, 1000],
[2000, 0.6],
[3000, 0.8],
[1.0, 4000],
])
assert_almost_equal(loss0, 0.01 / 4)
y_values = [0.0, 0.5, 0.8, 1.0]
adjusted_y = loss_obj.encode_y(
y_values, output_indices=[0, 1, 1, 0], inequalities=["=", "<", "<", "<"])
loss0 = evaluate_loss(
loss_obj.loss,
adjusted_y,
[
[0.1, 1000],
[2000, 0.6],
[3000, 0.8],
[1.0, 4000],
])
assert_almost_equal(loss0, 0.02 / 4)