def test(self):
sequences = [
ReceptorSequence(
amino_acid_sequence="AAACCC",
nucleotide_sequence="AAACCC",
identifier="1",
metadata=SequenceMetadata(custom_params={"l1": 1})),
ReceptorSequence(
amino_acid_sequence="ACACAC",
nucleotide_sequence="ACACAC",
identifier="2",
metadata=SequenceMetadata(custom_params={"l1": 2})),
ReceptorSequence(
amino_acid_sequence="CCCAAA",
nucleotide_sequence="CCCAAA",
identifier="3",
metadata=SequenceMetadata(custom_params={"l1": 1})),
ReceptorSequence(
amino_acid_sequence="AAACCC",
nucleotide_sequence="AAACCC",
identifier="4",
metadata=SequenceMetadata(custom_params={"l1": 2})),
ReceptorSequence(
amino_acid_sequence="ACACAC",
nucleotide_sequence="ACACAC",
identifier="5",
metadata=SequenceMetadata(custom_params={"l1": 1})),
ReceptorSequence(
amino_acid_sequence="CCCAAA",
nucleotide_sequence="CCCAAA",
identifier="6",
metadata=SequenceMetadata(custom_params={"l1": 2})),
ReceptorSequence(
amino_acid_sequence="AAACCC",
nucleotide_sequence="AAACCC",
identifier="7",
metadata=SequenceMetadata(custom_params={"l1": 1})),
ReceptorSequence(
amino_acid_sequence="ACACAC",
nucleotide_sequence="ACACAC",
identifier="8",
metadata=SequenceMetadata(custom_params={"l1": 2})),
ReceptorSequence(
amino_acid_sequence="CCCAAA",
nucleotide_sequence="CCCAAA",
identifier="9",
metadata=SequenceMetadata(custom_params={"l1": 1}))
]
path = EnvironmentSettings.tmp_test_path / "kmrefreqseqfacencoder/"
PathBuilder.build(path)
filename = path / "sequences.pkl"
with open(filename, "wb") as file:
pickle.dump(sequences, file)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
dataset = SequenceDataset(labels={"l1": [1, 2]},
filenames=[filename],
identifier="d2")
encoder = KmerFreqSequenceEncoder.build_object(
dataset, **{
"normalization_type":
NormalizationType.RELATIVE_FREQUENCY.name,
"reads": ReadsType.UNIQUE.name,
"sequence_encoding": SequenceEncodingType.CONTINUOUS_KMER.name,
"sequence_type": SequenceType.NUCLEOTIDE.name,
"k": 3
})
encoded_dataset = encoder.encode(
dataset,
EncoderParams(result_path=path / "2/",
label_config=lc,
pool_size=2,
learn_model=True,
model={},
filename="dataset.csv"))
self.assertEqual(9, encoded_dataset.encoded_data.examples.shape[0])
self.assertTrue(
all(identifier in encoded_dataset.encoded_data.example_ids for
identifier in ['1', '2', '3', '4', '5', '6', '7', '8', '9']))
self.assertTrue(
numpy.array_equal(encoded_dataset.encoded_data.examples[0].A,
encoded_dataset.encoded_data.examples[3].A))
shutil.rmtree(path)
def test(self):
receptors = [
TCABReceptor(alpha=ReceptorSequence(amino_acid_sequence="AAACCC"),
beta=ReceptorSequence(amino_acid_sequence="AAACCC"),
identifier="1"),
TCABReceptor(alpha=ReceptorSequence(amino_acid_sequence="AAA"),
beta=ReceptorSequence(amino_acid_sequence="CCC"),
identifier="2"),
TCABReceptor(alpha=ReceptorSequence(amino_acid_sequence="AAACCC"),
beta=ReceptorSequence(amino_acid_sequence="AAACCC"),
identifier="3"),
TCABReceptor(alpha=ReceptorSequence(amino_acid_sequence="AAA"),
beta=ReceptorSequence(amino_acid_sequence="CCC"),
identifier="4")
]
path = EnvironmentSettings.tmp_test_path / "kmer_receptor_frequency/"
PathBuilder.build(path)
filename = path / "receptors.pkl"
with open(filename, "wb") as file:
pickle.dump(receptors, file)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
dataset = ReceptorDataset(labels={"l1": [1, 2]},
filenames=[filename],
identifier="d1")
encoder = KmerFreqReceptorEncoder.build_object(
dataset, **{
"normalization_type":
NormalizationType.RELATIVE_FREQUENCY.name,
"reads": ReadsType.UNIQUE.name,
"sequence_encoding": SequenceEncodingType.CONTINUOUS_KMER.name,
"k": 3
})
encoded_dataset = encoder.encode(
dataset,
EncoderParams(result_path=path / "2/",
label_config=lc,
pool_size=2,
learn_model=True,
model={},
filename="dataset.csv",
encode_labels=False))
self.assertEqual(4, encoded_dataset.encoded_data.examples.shape[0])
self.assertTrue(
all(identifier in encoded_dataset.encoded_data.example_ids
for identifier in ['1', '2', '3', '4']))
self.assertTrue(
numpy.array_equal(encoded_dataset.encoded_data.examples[0].A,
encoded_dataset.encoded_data.examples[2].A))
self.assertTrue(
all(feature_name in encoded_dataset.encoded_data.feature_names
for feature_name in ["alpha_AAA", "alpha_AAC", "beta_CCC"]))
shutil.rmtree(path)
