add custom loss tests

test/test_custom_loss.py

+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
+
+
+def evaluate_loss(loss, y_true, y_pred):
+    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)
+
+
+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)
+    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, 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)
+
+    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, 0.03)
+