Skip to content
Snippets Groups Projects
Select Git revision
  • publish
  • master default protected
  • arch2
  • recover_lost_data
  • manuscript
  • improved-allele-parsing
  • 202010-phospho
  • gh-pages
  • pre-2.0
  • 1.7.0c
  • 3-flank-variant
  • 1.7.0b
  • 1.7.0
  • pre-presentation-refactor2
  • 20200121_pre_presentation_refactor
  • issue-149
  • 201911
  • ligandome-prediction
  • docs-pan-motifs
  • v1.3
  • pre-2.1
  • v2.0.1
  • v2.0.0
  • pre-2.0
  • 1.6.1
  • pre-1.7.0
  • pre_presentation_refactor
  • 1.6.0
  • 1.4.0
  • pre-1.4.0
  • pan-dev1
  • 1.2.2
  • 1.2.0
  • pre-1.2.1
  • pre-1.2
  • pre-1.1
  • 1.0.0
  • pre-1.0
  • 0.9.2
  • 0.9.1
40 results
Blame Permalink
test_custom_loss.py 2.38 KiB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73 






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):
    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)
    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)