Newer
Older
import pandas
import numpy
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
MultiallelicMassSpecBatchGenerator)
from numpy.testing import assert_equal
def test_basic():
planner = MultiallelicMassSpecBatchGenerator(
hyperparameters=dict(
batch_generator_validation_split=0.2,
batch_generator_batch_size=10,
batch_generator_affinity_fraction=0.5))
exp1_alleles = ["HLA-A*03:01", "HLA-B*07:02", "HLA-C*02:01"]
exp2_alleles = ["HLA-A*02:01", "HLA-B*27:01", "HLA-C*02:01"]
df = pandas.DataFrame(dict(
affinities_mask=([True] * 4) + ([False] * 6),
experiment_names=([None] * 4) + (["exp1"] * 2) + (["exp2"] * 4),
alleles_matrix=[
["HLA-A*02:01", None, None],
["HLA-A*02:01", None, None],
["HLA-A*03:01", None, None],
["HLA-A*03:01", None, None],
exp1_alleles,
exp1_alleles,
exp2_alleles,
exp2_alleles,
exp2_alleles,
exp2_alleles,
],
is_binder=[
True, True, False, False, True, False, True, False, True, False,
]))
planner.plan(**df.to_dict("list"))
print(planner.summary())
(train_iter, test_iter) = planner.get_train_and_test_generators(
x_dict={
"idx": numpy.arange(len(df)),
},
y_list=[])
for (kind, it) in [("train", train_iter), ("test", test_iter)]:
for (i, (x_item, y_item)) in enumerate(it):
idx = x_item["idx"]
df.loc[idx, "kind"] = kind
df.loc[idx, "idx"] = idx
df.loc[idx, "batch"] = i
df["idx"] = df.idx.astype(int)
df["batch"] = df.batch.astype(int)
print(df)
for ((kind, batch_num), batch_df) in df.groupby(["kind", "batch"]):
if not batch_df.affinities_mask.all():
# Test each batch has at most one multiallelic ms experiment.
assert_equal(
batch_df.loc[
~batch_df.affinities_mask
].experiment_names.nunique(), 1)
#import ipdb;ipdb.set_trace()