def create_dataset(self, path, dataset_size: int = 50):
sequences = []
for i in range(dataset_size):
if i % 2 == 0:
sequences.append(
ReceptorSequence(
amino_acid_sequence="AAACCC",
identifier=str(i),
metadata=SequenceMetadata(custom_params={"l1": 1})))
else:
sequences.append(
ReceptorSequence(
amino_acid_sequence="ACACAC",
identifier=str(i),
metadata=SequenceMetadata(custom_params={"l1": 2})))
PathBuilder.build(path)
filename = "{}sequences.pkl".format(path)
with open(filename, "wb") as file:
pickle.dump(sequences, file)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
dataset = SequenceDataset(params={"l1": [1, 2]},
filenames=[filename],
identifier="d1")
return dataset
def test_encode_sequence(self):
sequence = ReceptorSequence(
amino_acid_sequence="AAA",
metadata=SequenceMetadata(frame_type="OUT"))
enc = IdentitySequenceEncoder()
self.assertEqual(
enc.encode_sequence(
sequence,
EncoderParams(model={},
label_config=LabelConfiguration(),
result_path="")), ["AAA"])
sequence = ReceptorSequence(
amino_acid_sequence="AAA",
metadata=SequenceMetadata(frame_type="STOP"))
enc = IdentitySequenceEncoder()
self.assertEqual(
enc.encode_sequence(
sequence,
EncoderParams(model={},
label_config=LabelConfiguration(),
result_path="")), ["AAA"])
sequence = ReceptorSequence(amino_acid_sequence="AAA",
metadata=SequenceMetadata(frame_type="IN"))
enc = IdentitySequenceEncoder()
self.assertEqual(["AAA"],
enc.encode_sequence(
sequence,
EncoderParams(model={},
label_config=LabelConfiguration(),
result_path="")))
def _create_label_config(self, instruction: dict, dataset: Dataset,
instruction_key: str) -> LabelConfiguration:
labels = instruction["labels"]
self._check_label_format(labels, instruction_key)
label_config = LabelConfiguration()
for label in labels:
label_name = label if isinstance(label, str) else list(
label.keys())[0]
positive_class = label[label_name]['positive_class'] if isinstance(
label, dict) else None
if dataset.params is not None and label_name in dataset.params:
label_values = dataset.params[label_name]
elif hasattr(dataset, "get_metadata"):
label_values = list(
set(dataset.get_metadata([label_name])[label_name]))
else:
label_values = []
warnings.warn(
f"{TrainMLModelParser.__name__}: for instruction {instruction_key}, label values could not be recovered for label "
f"{label}, using empty list instead. This could cause problems with some encodings. "
f"If that might be the case, check if the dataset {dataset.name} has been properly loaded."
)
label_config.add_label(label_name,
label_values,
positive_class=positive_class)
return label_config
def test(self):
path = EnvironmentSettings.tmp_test_path + "integration_sequence_classification/"
dataset = self.create_dataset(path)
os.environ["cache_type"] = "test"
encoder_params = {
"normalization_type": NormalizationType.RELATIVE_FREQUENCY.name,
"reads": ReadsType.UNIQUE.name,
"sequence_encoding": SequenceEncodingType.CONTINUOUS_KMER.name,
"k": 3
}
hp_setting = HPSetting(encoder=KmerFrequencyEncoder.build_object(
dataset, **encoder_params),
encoder_params=encoder_params,
ml_method=LogisticRegression(),
ml_params={
"model_selection_cv": False,
"model_selection_n_folds": -1
},
preproc_sequence=[])
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
instruction = TrainMLModelInstruction(
dataset, GridSearch([hp_setting]), [hp_setting],
SplitConfig(SplitType.RANDOM, 1, 0.5, reports=ReportConfig()),
SplitConfig(SplitType.RANDOM, 1, 0.5, reports=ReportConfig()),
{Metric.BALANCED_ACCURACY}, Metric.BALANCED_ACCURACY, lc, path)
result = instruction.run(result_path=path)
shutil.rmtree(path)
def test_encode_sequence(self):
seq = ReceptorSequence(amino_acid_sequence="CASSVFRTY")
result = KmerSequenceEncoder.encode_sequence(
seq,
EncoderParams(model={"k": 3},
label_config=LabelConfiguration(),
result_path="",
pool_size=4))
self.assertTrue("CAS" in result)
self.assertTrue("ASS" in result)
self.assertTrue("SSV" in result)
self.assertTrue("SVF" in result)
self.assertTrue("VFR" in result)
self.assertTrue("FRT" in result)
self.assertTrue("RTY" in result)
self.assertEqual(7, len(result))
self.assertEqual(
KmerSequenceEncoder.encode_sequence(
ReceptorSequence(amino_acid_sequence="AC"),
EncoderParams(model={"k": 3},
label_config=LabelConfiguration(),
result_path="",
pool_size=4)), None)
def __init__(self, split_index: int, train_val_dataset, test_dataset, path: str, label_configuration: LabelConfiguration):
self.split_index = split_index
self.train_val_dataset = train_val_dataset
self.test_dataset = test_dataset
self.path = path
self.train_val_data_reports = []
self.test_data_reports = []
# computed
self.label_states = {label: HPLabelState(label, label_configuration.get_auxiliary_labels(label))
for label in label_configuration.get_labels_by_name()}
def _construct_test_repertoiredataset(self, path, positional):
receptors1 = ReceptorSequenceList()
receptors2 = ReceptorSequenceList()
if positional:
[
receptors1.append(seq) for seq in [
ReceptorSequence("AAAAAAAAAAAAAAAAA", identifier="1"),
ReceptorSequence("AAAAAAAAAAAAAAAAA", identifier="1")
]
]
[
receptors2.append(seq)
for seq in [ReceptorSequence("TTTTTTTTTTTTT", identifier="1")]
]
else:
[
receptors1.append(seq) for seq in [
ReceptorSequence("AAAA", identifier="1"),
ReceptorSequence("ATA", identifier="2"),
ReceptorSequence("ATA", identifier='3')
]
]
[
receptors2.append(seq) for seq in [
ReceptorSequence("ATA", identifier="1"),
ReceptorSequence("TAA", identifier="2")
]
]
rep1 = Repertoire.build_from_sequence_objects(receptors1,
metadata={
"l1": 1,
"l2": 2,
"subject_id": "1"
},
path=path)
rep2 = Repertoire.build_from_sequence_objects(receptors2,
metadata={
"l1": 0,
"l2": 3,
"subject_id": "2"
},
path=path)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
lc.add_label("l2", [0, 3])
dataset = RepertoireDataset(repertoires=[rep1, rep2])
return dataset, lc
def construct_test_flatten_dataset(self, path):
sequences = [ReceptorSequence(amino_acid_sequence="AAATTT", identifier="1", metadata=SequenceMetadata(custom_params={"l1": 1})),
ReceptorSequence(amino_acid_sequence="ATATAT", identifier="2", metadata=SequenceMetadata(custom_params={"l1": 2}))]
PathBuilder.build(path)
filename = "{}sequences.pkl".format(path)
with open(filename, "wb") as file:
pickle.dump(sequences, file)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
return SequenceDataset(params={"l1": [1, 2]}, filenames=[filename], identifier="d1")
def test_run(self):
path = EnvironmentSettings.tmp_test_path + "explanalysisprocintegration/"
PathBuilder.build(path)
os.environ["cache_type"] = "test"
dataset = self.create_dataset(path)
label_config = LabelConfiguration()
label_config.add_label("l1", [0, 1])
label_config.add_label("l2", [2, 3])
file_content = """complex.id Gene CDR3 V J Species MHC A MHC B MHC class Epitope Epitope gene Epitope species Reference Method Meta CDR3fix Score
100a TRA AAAC TRAV12 TRAJ1 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV
"""
with open(path + "refs.tsv", "w") as file:
file.writelines(file_content)
refs = {
"params": {
"path": path + "refs.tsv",
"region_type": "FULL_SEQUENCE"
},
"format": "VDJdb"
}
units = {
"named_analysis_4":
ExploratoryAnalysisUnit(
dataset=dataset,
report=DesignMatrixExporter(),
label_config=label_config,
encoder=MatchedSequencesRepertoireEncoder.build_object(
dataset, **{
"max_edit_distance": 1,
"reference": refs
}))
}
process = ExploratoryAnalysisInstruction(units, name="exp")
process.run(path + "results/")
self.assertTrue(
os.path.isfile(
path +
"results/exp/analysis_named_analysis_4/report/design_matrix.csv"
))
shutil.rmtree(path)
def test_sequence_flattened(self):
path = EnvironmentSettings.root_path + "test/tmp/onehot_seq_flat/"
PathBuilder.build(path)
dataset = self.construct_test_flatten_dataset(path)
encoder = OneHotEncoder.build_object(dataset, **{"use_positional_info": False, "distance_to_seq_middle": None, "flatten": True})
encoded_data = encoder.encode(dataset, EncoderParams(
result_path=path,
label_config=LabelConfiguration([Label(name="l1", values=[1, 0], positive_class="1")]),
pool_size=1,
learn_model=True,
model={},
filename="dataset.pkl"
))
self.assertTrue(isinstance(encoded_data, SequenceDataset))
onehot_a = [1.0] + [0.0] * 19
onehot_t = [0.0] * 16 + [1.0] + [0] * 3
self.assertListEqual(list(encoded_data.encoded_data.examples[0]), onehot_a+onehot_a+onehot_a+onehot_t+onehot_t+onehot_t)
self.assertListEqual(list(encoded_data.encoded_data.examples[1]), onehot_a+onehot_t+onehot_a+onehot_t+onehot_a+onehot_t)
self.assertListEqual(list(encoded_data.encoded_data.feature_names), [f"{pos}_{char}" for pos in range(6) for char in EnvironmentSettings.get_sequence_alphabet()])
shutil.rmtree(path)
def test_encode(self):
path = EnvironmentSettings.tmp_test_path + "abundance_encoder/"
PathBuilder.build(path)
repertoires, metadata = RepertoireBuilder.build([["GGG", "III", "LLL", "MMM"],
["DDD", "EEE", "FFF", "III", "LLL", "MMM"],
["CCC", "FFF", "MMM"],
["AAA", "CCC", "EEE", "FFF", "LLL", "MMM"]],
labels={"l1": [True, True, False, False]}, path=path)
dataset = RepertoireDataset(repertoires=repertoires, metadata_file=metadata, identifier="1")
encoder = SequenceAbundanceEncoder.build_object(dataset, **{
"comparison_attributes": ["sequence_aas"],
"p_value_threshold": 0.4, "sequence_batch_size": 4, "repertoire_batch_size": 8
})
label_config = LabelConfiguration([Label("l1", [True, False], positive_class=True)])
encoded_dataset = encoder.encode(dataset, EncoderParams(result_path=path, label_config=label_config))
self.assertTrue(np.array_equal(np.array([[1, 4], [1, 6], [0, 3], [0, 6]]), encoded_dataset.encoded_data.examples))
encoder.p_value_threshold = 0.05
encoded_dataset = encoder.encode(dataset, EncoderParams(result_path=path, label_config=label_config))
self.assertTrue(np.array_equal(np.array([[0, 4], [0, 6], [0, 3], [0, 6]]), encoded_dataset.encoded_data.examples))
shutil.rmtree(path)
def _prepare_optional_params(self, analysis: dict,
symbol_table: SymbolTable) -> dict:
params = {}
dataset = symbol_table.get(analysis["dataset"])
if all(key in analysis for key in ["encoding", "labels"]):
params["encoder"] = symbol_table.get(analysis["encoding"]) \
.build_object(dataset, **symbol_table.get_config(analysis["encoding"])["encoder_params"])
params["label_config"] = LabelConfiguration()
for label in analysis["labels"]:
label_values = self._get_label_values(label, dataset)
params["label_config"].add_label(label, label_values)
elif any(key in analysis for key in ["encoding", "labels"]):
raise KeyError(
"ExploratoryAnalysisParser: keys for analyses are not properly defined. "
"If encoding is defined, labels have to be defined as well and vice versa."
)
if "preprocessing_sequence" in analysis:
params["preprocessing_sequence"] = symbol_table.get(
analysis["preprocessing_sequence"])
if "number_of_processes" in analysis:
params["number_of_processes"] = analysis["number_of_processes"]
return params
def test_run(self):
path = EnvironmentSettings.root_path + "test/tmp/dataencoder/"
PathBuilder.build(path)
rep1 = Repertoire.build_from_sequence_objects(
[ReceptorSequence("AAA", identifier="1")],
metadata={
"l1": 1,
"l2": 2
},
path=path)
rep2 = Repertoire.build_from_sequence_objects(
[ReceptorSequence("ATA", identifier="2")],
metadata={
"l1": 0,
"l2": 3
},
path=path)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
lc.add_label("l2", [0, 3])
dataset = RepertoireDataset(repertoires=[rep1, rep2])
encoder = Word2VecEncoder.build_object(
dataset, **{
"k": 3,
"model_type": ModelType.SEQUENCE.name,
"vector_size": 6
})
res = DataEncoder.run(
DataEncoderParams(dataset=dataset,
encoder=encoder,
encoder_params=EncoderParams(
model={},
pool_size=2,
label_config=lc,
result_path=path,
filename="dataset.csv"),
store_encoded_data=False))
self.assertTrue(isinstance(res, RepertoireDataset))
self.assertTrue(res.encoded_data.examples.shape[0] == 2)
shutil.rmtree(path)
def test_encode(self):
test_path = EnvironmentSettings.root_path + "test/tmp/w2v/"
PathBuilder.build(test_path)
sequence1 = ReceptorSequence("CASSVFA", identifier="1")
sequence2 = ReceptorSequence("CASSCCC", identifier="2")
metadata1 = {"T1D": "T1D", "subject_id": "1"}
rep1 = Repertoire.build_from_sequence_objects([sequence1, sequence2],
test_path, metadata1)
metadata2 = {"T1D": "CTL", "subject_id": "2"}
rep2 = Repertoire.build_from_sequence_objects([sequence1], test_path,
metadata2)
dataset = RepertoireDataset(repertoires=[rep1, rep2])
label_configuration = LabelConfiguration()
label_configuration.add_label("T1D", ["T1D", "CTL"])
config_params = EncoderParams(model={},
learn_model=True,
result_path=test_path,
label_config=label_configuration,
filename="dataset.pkl")
encoder = Word2VecEncoder.build_object(
dataset, **{
"k": 3,
"model_type": "sequence",
"vector_size": 16
})
encoded_dataset = encoder.encode(dataset=dataset, params=config_params)
self.assertIsNotNone(encoded_dataset.encoded_data)
self.assertTrue(encoded_dataset.encoded_data.examples.shape[0] == 2)
self.assertTrue(encoded_dataset.encoded_data.examples.shape[1] == 16)
self.assertTrue(len(encoded_dataset.encoded_data.labels["T1D"]) == 2)
self.assertTrue(encoded_dataset.encoded_data.labels["T1D"][0] == "T1D")
self.assertTrue(isinstance(encoder, W2VRepertoireEncoder))
shutil.rmtree(test_path)
def test_run(self):
path = EnvironmentSettings.tmp_test_path + "mlapplicationtest/"
PathBuilder.build(path)
dataset = RandomDatasetGenerator.generate_repertoire_dataset(
50, {5: 1}, {5: 1}, {"l1": {
1: 0.5,
2: 0.5
}}, path + 'dataset/')
ml_method = LogisticRegression()
encoder = KmerFreqRepertoireEncoder(
NormalizationType.RELATIVE_FREQUENCY,
ReadsType.UNIQUE,
SequenceEncodingType.CONTINUOUS_KMER,
3,
scale_to_zero_mean=True,
scale_to_unit_variance=True)
label_config = LabelConfiguration([Label("l1", [1, 2])])
enc_dataset = encoder.encode(
dataset,
EncoderParams(result_path=path,
label_config=label_config,
filename="tmp_enc_dataset.pickle",
pool_size=4))
ml_method.fit(enc_dataset.encoded_data, 'l1')
hp_setting = HPSetting(
encoder, {
"normalization_type": "relative_frequency",
"reads": "unique",
"sequence_encoding": "continuous_kmer",
"k": 3,
"scale_to_zero_mean": True,
"scale_to_unit_variance": True
}, ml_method, {}, [], 'enc1', 'ml1')
PathBuilder.build(path + 'result/instr1/')
shutil.copy(path + 'dict_vectorizer.pickle',
path + 'result/instr1/dict_vectorizer.pickle')
shutil.copy(path + 'scaler.pickle',
path + 'result/instr1/scaler.pickle')
ml_app = MLApplicationInstruction(dataset, label_config, hp_setting, 4,
"instr1", False)
ml_app.run(path + 'result/')
predictions_path = path + "result/instr1/predictions.csv"
self.assertTrue(os.path.isfile(predictions_path))
df = pd.read_csv(predictions_path)
self.assertEqual(50, df.shape[0])
shutil.rmtree(path)
def test_encode(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path + "atchley_kmer_encoding/")
dataset = RandomDatasetGenerator.generate_repertoire_dataset(3, {1: 1}, {4: 1}, {"l1": {True: 0.4, False: 0.6}}, path + "dataset/")
encoder = AtchleyKmerEncoder.build_object(dataset, **{"k": 2, "skip_first_n_aa": 1, "skip_last_n_aa": 1, "abundance": "RELATIVE_ABUNDANCE",
"normalize_all_features": False})
encoded_dataset = encoder.encode(dataset, EncoderParams(path + "result/", LabelConfiguration(labels=[Label("l1")])))
self.assertEqual((3, 11, 3), encoded_dataset.encoded_data.examples.shape)
self.assertEqual(0., encoded_dataset.encoded_data.examples[0, -1, 0])
shutil.rmtree(path)
def run_setting(state: TrainMLModelState, hp_setting, train_dataset, val_dataset, split_index: int,
current_path: str, label: str, assessment_index: int):
hp_item = MLProcess(train_dataset=train_dataset, test_dataset=val_dataset, encoding_reports=state.selection.reports.encoding_reports.values(),
label_config=LabelConfiguration([state.label_configuration.get_label_object(label)]), report_context=state.context,
number_of_processes=state.number_of_processes, metrics=state.metrics, optimization_metric=state.optimization_metric,
ml_reports=state.selection.reports.model_reports.values(), label=label, path=current_path, hp_setting=hp_setting,
store_encoded_data=state.store_encoded_data)\
.run(split_index)
state.assessment_states[assessment_index].label_states[label].selection_state.hp_items[hp_setting.get_key()].append(hp_item)
return hp_item.performance[state.optimization_metric.name.lower()] if hp_item.performance is not None else None
def test_run(self):
path = EnvironmentSettings.root_path + "test/tmp/mlmethodassessment/"
PathBuilder.build(path)
dataset = RepertoireDataset(repertoires=RepertoireBuilder.build([["AA"], ["CC"], ["AA"], ["CC"], ["AA"], ["CC"], ["AA"], ["CC"], ["AA"], ["CC"], ["AA"], ["CC"]], path)[0])
dataset.encoded_data = EncodedData(
examples=np.array([[1, 1], [1, 1], [3, 3], [1, 1], [1, 1], [3, 3], [1, 1], [1, 1], [3, 3], [1, 1], [1, 1], [3, 3]]),
labels={"l1": [1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], "l2": [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]}
)
label_config = LabelConfiguration()
label_config.add_label("l1", [1, 3])
method1 = LogisticRegression()
method1.fit(dataset.encoded_data, label_name='l1')
res = MLMethodAssessment.run(MLMethodAssessmentParams(
dataset=dataset,
method=method1,
metrics={Metric.ACCURACY, Metric.BALANCED_ACCURACY, Metric.F1_MACRO},
optimization_metric=Metric.LOG_LOSS,
predictions_path=EnvironmentSettings.root_path + "test/tmp/mlmethodassessment/predictions.csv",
label="l1",
ml_score_path=EnvironmentSettings.root_path + "test/tmp/mlmethodassessment/ml_score.csv",
split_index=1,
path=EnvironmentSettings.root_path + "test/tmp/mlmethodassessment/"
))
self.assertTrue(isinstance(res, dict))
self.assertTrue(res[Metric.LOG_LOSS.name.lower()] <= 0.1)
self.assertTrue(os.path.isfile(EnvironmentSettings.root_path + "test/tmp/mlmethodassessment/ml_score.csv"))
df = pd.read_csv(EnvironmentSettings.root_path + "test/tmp/mlmethodassessment/ml_score.csv")
self.assertTrue(df.shape[0] == 1)
df = pd.read_csv(EnvironmentSettings.root_path + "test/tmp/mlmethodassessment/predictions.csv")
self.assertEqual(12, df.shape[0])
shutil.rmtree(EnvironmentSettings.root_path + "test/tmp/mlmethodassessment/")
def test_run(self):
path = EnvironmentSettings.root_path + "test/tmp/smmodel/"
PathBuilder.build(path)
repertoires, metadata = RepertoireBuilder.build([["AAA", "CCC"], ["TTTT"], ["AAA", "CCC"], ["TTTT"],
["AAA", "CCC"], ["TTTT"], ["AAA", "CCC"], ["TTTT"],
["AAA", "CCC"], ["TTTT"], ["AAA", "CCC"], ["TTTT"],
["AAA", "CCC"], ["TTTT"], ["AAA", "CCC"], ["TTTT"],
["AAA", "CCC"], ["TTTT"], ["AAA", "CCC"], ["TTTT"],
["AAA", "CCC"], ["TTTT"], ["AAA", "CCC"], ["TTTT"],
["AAA", "CCC"], ["TTTT"], ["AAA", "CCC"], ["TTTT"],
["AAA", "CCC"], ["TTTT"], ["AAA", "CCC"], ["TTTT"]], path,
{"default": [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,
1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2]})
dataset = RepertoireDataset(repertoires=repertoires,
params={"default": [1, 2]},
metadata_file=metadata)
label_config = LabelConfiguration()
label_config.add_label("default", [1, 2])
hp_settings = [HPSetting(Word2VecEncoder.build_object(dataset, **{"vector_size": 8, "model_type": ModelType.SEQUENCE.name, "k": 3}),
{"vector_size": 8, "model_type": ModelType.SEQUENCE.name, "k": 3},
LogisticRegression(),
{"model_selection_cv": False, "model_selection_n_folds": -1}, [])]
split_config_assessment = SplitConfig(SplitType.RANDOM, 1, 0.5, ReportConfig())
split_config_selection = SplitConfig(SplitType.RANDOM, 1, 0.5, ReportConfig())
instruction = TrainMLModelInstruction(dataset, GridSearch(hp_settings), hp_settings,
split_config_assessment,
split_config_selection,
{Metric.BALANCED_ACCURACY}, Metric.BALANCED_ACCURACY,
label_config, path)
semantic_model = SemanticModel([instruction], path)
semantic_model.run()
shutil.rmtree(path)
def test_encode_sequence(self):
sequence = ReceptorSequence("AHCDE", None, None)
kmers = IMGTGappedKmerEncoder.encode_sequence(
sequence,
EncoderParams(model={
"k_left": 1,
"max_gap": 1
},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual(
{
'AH///105', 'HC///106', 'CD///107', 'DE///116', 'A.C///105',
'H.D///106', 'C.E///107'
}, set(kmers))
sequence = ReceptorSequence("CASSPRERATYEQCAY", None, None)
kmers = IMGTGappedKmerEncoder.encode_sequence(
sequence,
EncoderParams(model={
"k_left": 1,
"max_gap": 1
},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual(
{
'CA///105', 'AS///106', 'SS///107', 'SP///108', 'PR///109',
'RE///110', 'ER///111', 'RA///111.001', 'AT///112.002',
'TY///112.001', 'YE///112', 'EQ///113', 'QC///114', 'CA///115',
'AY///116', 'C.S///105', 'A.S///106', 'S.P///107', 'S.R///108',
'P.E///109', 'R.R///110', 'E.A///111', 'R.T///111.001',
'A.Y///112.002', 'T.E///112.001', 'Y.Q///112', 'E.C///113',
'Q.A///114', 'C.Y///115'
}, set(kmers))
def test_encode_sequence(self):
sequence = ReceptorSequence("CASSPRERATYEQCASSPRERATYEQCASSPRERATYEQ", None, None)
result = IMGTKmerSequenceEncoder.encode_sequence(sequence, EncoderParams(
model={"k": 3},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual({'CAS///105', 'ASS///106', 'SSP///107', 'SPR///108', 'PRE///109', 'RER///110', 'ERA///111',
'RAT///111.001', 'ATY///111.002', 'TYE///111.003', 'YEQ///111.004', 'EQC///111.005',
'QCA///111.006', 'CAS///111.007', 'ASS///111.008', 'SSP///111.009', 'SPR///111.01',
'PRE///111.011', 'RER///111.012', 'ERA///111.013', 'RAT///112.013', 'ATY///112.012',
'TYE///112.011', 'YEQ///112.01', 'EQC///112.009', 'QCA///112.008', 'CAS///112.007',
'ASS///112.006', 'SSP///112.005', 'SPR///112.004', 'PRE///112.003', 'RER///112.002',
'ERA///112.001', 'RAT///112', 'ATY///113', 'TYE///114', 'YEQ///115'},
set(result))
self.assertEqual(len(result), len(sequence.get_sequence()) - 3 + 1)
sequence = ReceptorSequence("AHCDE", None, None)
result = IMGTKmerSequenceEncoder.encode_sequence(sequence, EncoderParams(
model={"k": 3},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual({'AHC///105', 'HCD///106', 'CDE///107'},
set(result))
self.assertEqual(len(result), len(sequence.get_sequence()) - 3 + 1)
self.assertEqual(
IMGTKmerSequenceEncoder.encode_sequence(
sequence,
EncoderParams(model={"k": 25},
label_config=LabelConfiguration(),
result_path="")
),
None
)
def test_generate(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path +
"tcrdist_motif_discovery/")
dataset_path = self._create_dataset(path)
dataset = SingleLineReceptorImport.import_dataset(
{
"path":
dataset_path,
"result_path":
path + "dataset/",
"separator":
",",
"columns_to_load": [
"subject", "epitope", "count", "v_a_gene", "j_a_gene",
"cdr3_a_aa", "v_b_gene", "j_b_gene", "cdr3_b_aa",
"clone_id", "cdr3_a_nucseq", "cdr3_b_nucseq"
],
"column_mapping": {
"cdr3_a_aa": "alpha_amino_acid_sequence",
"cdr3_b_aa": "beta_amino_acid_sequence",
"cdr3_a_nucseq": "alpha_nucleotide_sequence",
"cdr3_b_nucseq": "beta_nucleotide_sequence",
"v_a_gene": "alpha_v_gene",
"v_b_gene": "beta_v_gene",
"j_a_gene": "alpha_j_gene",
"j_b_gene": "beta_j_gene",
"clone_id": "identifier"
},
"receptor_chains":
"TRA_TRB",
"region_type":
"IMGT_CDR3",
"sequence_file_size":
50000,
"organism":
"mouse"
}, 'd1')
dataset = TCRdistEncoder(8).encode(
dataset,
EncoderParams(f"{path}result/",
LabelConfiguration([Label("epitope")])))
report = TCRdistMotifDiscovery(dataset, path + "report/",
"report name", 8)
report.generate_report()
shutil.rmtree(path)
def _construct_test_dataset(self, path, dataset_size: int = 50):
receptors = [
TCABReceptor(alpha=ReceptorSequence(amino_acid_sequence="AAAA"),
beta=ReceptorSequence(amino_acid_sequence="ATA"),
metadata={"l1": 1},
identifier=str("1")),
TCABReceptor(alpha=ReceptorSequence(amino_acid_sequence="ATA"),
beta=ReceptorSequence(amino_acid_sequence="ATT"),
metadata={"l1": 2},
identifier=str("2"))
]
PathBuilder.build(path)
filename = "{}receptors.pkl".format(path)
with open(filename, "wb") as file:
pickle.dump(receptors, file)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
dataset = ReceptorDataset(params={"l1": [1, 2]},
filenames=[filename],
identifier="d1")
return dataset, lc
def parse(self, key: str, instruction: dict, symbol_table: SymbolTable, path: str) -> MLApplicationInstruction:
location = MLApplicationParser.__name__
ParameterValidator.assert_keys(instruction.keys(), ['type', 'dataset', 'label', 'pool_size', 'config_path', 'store_encoded_data'], location, key)
ParameterValidator.assert_in_valid_list(instruction['dataset'], symbol_table.get_keys_by_type(SymbolType.DATASET), location, f"{key}: dataset")
ParameterValidator.assert_type_and_value(instruction['pool_size'], int, location, f"{key}: pool_size", min_inclusive=1)
ParameterValidator.assert_type_and_value(instruction['label'], str, location, f'{key}: label')
ParameterValidator.assert_type_and_value(instruction['config_path'], str, location, f'{key}: config_path')
ParameterValidator.assert_type_and_value(instruction['store_encoded_data'], bool, location, f'{key}: store_encoded_data')
hp_setting, label = self._parse_hp_setting(instruction, path, key)
instruction = MLApplicationInstruction(dataset=symbol_table.get(instruction['dataset']), name=key, pool_size=instruction['pool_size'],
label_configuration=LabelConfiguration([label]), hp_setting=hp_setting,
store_encoded_data=instruction['store_encoded_data'])
return instruction
def encode_dataset_by_kmer_freq(path_to_dataset_directory: str, result_path: str, metadata_path: str = None):
"""
encodes the repertoire dataset using KmerFrequencyEncoder
:param path_to_dataset_directory: path to directory containing all repertoire files with .tsv extension in MiXCR format
:param result_path: where to store the results
:param metadata_path: csv file with columns "filename", "subject_id", "disease" which is filled by default if value of argument is None,
otherwise any metadata csv file passed to the function, must include filename and subject_id columns,
and an arbitrary disease column
:return: encoded dataset with encoded data in encoded_dataset.encoded_data.examples
"""
if metadata_path is None:
metadata_path = generate_random_metadata(path_to_dataset_directory, result_path)
loader = MiXCRImport()
dataset = loader.import_dataset({
"is_repertoire": True,
"path": path_to_dataset_directory,
"metadata_file": metadata_path,
"region_type": "IMGT_CDR3", # import_dataset in only cdr3
"number_of_processes": 4, # number of parallel processes for loading the data
"result_path": result_path,
"separator": "\t",
"columns_to_load": ["cloneCount", "allVHitsWithScore", "allJHitsWithScore", "aaSeqCDR3", "nSeqCDR3"],
"column_mapping": {
"cloneCount": "counts",
"allVHitsWithScore": "v_genes",
"allJHitsWithScore": "j_genes"
},
}, "mixcr_dataset")
label_name = list(dataset.params.keys())[0] # label that can be used for ML prediction - by default: "disease" with values True/False
encoded_dataset = DataEncoder.run(DataEncoderParams(dataset, KmerFrequencyEncoder.build_object(dataset, **{
"normalization_type": "relative_frequency", # encode repertoire by the relative frequency of k-mers in repertoire
"reads": "unique", # count each sequence only once, do not use clonal count
"k": 2, # k-mer length
"sequence_encoding": "continuous_kmer" # split each sequence in repertoire to overlapping k-mers
}), EncoderParams(result_path=result_path,
label_config=LabelConfiguration([Label(label_name, dataset.params[label_name])])), False))
dataset_exporter = DesignMatrixExporter(dataset=encoded_dataset,
result_path=f"{result_path if result_path[:-1] == '/' else result_path+'/'}csv_exported/")
dataset_exporter.generate_report()
return encoded_dataset
def _create_state_object(self, path):
repertoires, metadata = RepertoireBuilder.build(sequences=[["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"],
["AAA", "CCC", "DDD"], ["AAA", "CCC", "DDD"]],
path=path,
labels={
"l1": [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,
1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],
"l2": [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]})
dataset = RepertoireDataset(repertoires=repertoires, metadata_file=metadata,
params={"l1": [1, 2], "l2": [0, 1]})
enc_params = {"k": 3, "model_type": ModelType.SEQUENCE.name, "vector_size": 4}
hp_settings = [HPSetting(Word2VecEncoder.build_object(dataset, **enc_params), enc_params,
LogisticRegression(),
{"model_selection_cv": False, "model_selection_n_folds": -1},
[])]
label_config = LabelConfiguration([Label("l1", [1, 2]), Label("l2", [0, 1])])
process = TrainMLModelInstruction(dataset, GridSearch(hp_settings), hp_settings,
SplitConfig(SplitType.RANDOM, 1, 0.5),
SplitConfig(SplitType.RANDOM, 1, 0.5),
{Metric.BALANCED_ACCURACY}, Metric.BALANCED_ACCURACY, label_config, path)
state = process.run(result_path=path)
return state
def create_dummy_data(self, path):
# Setting up dummy data
labels = {
"subject_id": ["subject_1", "subject_2", "subject_3"],
"label": ["yes", "yes", "no"]
}
metadata = {
"v_gene": "TRBV1",
"j_gene": "TRBJ1",
"chain": Chain.BETA.value
}
repertoires, metadata = RepertoireBuilder.build(
sequences=[["AAAA"], ["SSSS"], ["SSSS", "CCCC"]],
path=path,
labels=labels,
seq_metadata=[[{
**metadata, "count": 10
}], [{
**metadata, "count": 10
}], [{
**metadata, "count": 5
}, {
**metadata, "count": 5
}]],
subject_ids=labels["subject_id"])
dataset = RepertoireDataset(repertoires=repertoires)
label_config = LabelConfiguration()
label_config.add_label("subject_id", labels["subject_id"])
label_config.add_label("label", labels["label"])
file_content = """complex.id Gene CDR3 V J Species MHC A MHC B MHC class Epitope Epitope gene Epitope species Reference Method Meta CDR3fix Score
100 TRB AAAA TRBV1 TRBJ1 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CASSPPRVYSNGAGLAGVGWRNEQFF", "cdr3_old": "CASSPPRVYSNGAGLAGVGWRNEQFF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-1*01", "jStart": 21, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-4*01"} 0
200 TRB SSSS TRBV1 TRBJ1 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CASSWTWDAATLWGQGALGGANVLTF", "cdr3_old": "CASSWTWDAATLWGQGALGGANVLTF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-6*01", "jStart": 19, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-5*01"} 0"""
with open(path + "refs.tsv", "w") as file:
file.writelines(file_content)
reference_sequences = {
"params": {
"path": path + "refs.tsv",
"region_type": "FULL_SEQUENCE"
},
"format": "VDJdb"
}
return dataset, label_config, reference_sequences, labels
def test_encode(self):
file_content = """complex.id Gene CDR3 V J Species MHC A MHC B MHC class Epitope Epitope gene Epitope species Reference Method Meta CDR3fix Score
3050 TRB CASSPPRVYSNGAGLAGVGWRNEQFF TRBV5-4*01 TRBJ2-1*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CASSPPRVYSNGAGLAGVGWRNEQFF", "cdr3_old": "CASSPPRVYSNGAGLAGVGWRNEQFF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-1*01", "jStart": 21, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-4*01"} 0
15760 TRB CASSWTWDAATLWGQGALGGANVLTF TRBV5-5*01 TRBJ2-6*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CASSWTWDAATLWGQGALGGANVLTF", "cdr3_old": "CASSWTWDAATLWGQGALGGANVLTF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-6*01", "jStart": 19, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-5*01"} 0
3050 TRA CAAIYESRGSTLGRLYF TRAV13-1*01 TRAJ18*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CAAIYESRGSTLGRLYF", "cdr3_old": "CAAIYESRGSTLGRLYF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ18*01", "jStart": 7, "oldVEnd": -1, "oldVFixType": "FailedBadSegment", "oldVId": null, "vCanonical": true, "vEnd": 3, "vFixType": "ChangeSegment", "vId": "TRAV13-1*01"} 0
15760 TRA CALRLNNQGGKLIF TRAV9-2*01 TRAJ23*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CALRLNNQGGKLIF", "cdr3_old": "CALRLNNQGGKLIF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ23*01", "jStart": 6, "vCanonical": true, "vEnd": 3, "vFixType": "NoFixNeeded", "vId": "TRAV9-2*01"} 0
3051 TRB CASSPPRVYSNGAGLAGVGWRNEQFF TRBV5-4*01 TRBJ2-1*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CASSPPRVYSNGAGLAGVGWRNEQFF", "cdr3_old": "CASSPPRVYSNGAGLAGVGWRNEQFF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-1*01", "jStart": 21, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-4*01"} 0
15761 TRB CASSWTWDAATLWGQGALGGANVLTF TRBV5-5*01 TRBJ2-6*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CASSWTWDAATLWGQGALGGANVLTF", "cdr3_old": "CASSWTWDAATLWGQGALGGANVLTF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-6*01", "jStart": 19, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-5*01"} 0
3051 TRA CAAIYESRGSTLGRLYF TRAV13-1*01 TRAJ18*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CAAIYESRGSTLGRLYF", "cdr3_old": "CAAIYESRGSTLGRLYF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ18*01", "jStart": 7, "oldVEnd": -1, "oldVFixType": "FailedBadSegment", "oldVId": null, "vCanonical": true, "vEnd": 3, "vFixType": "ChangeSegment", "vId": "TRAV13-1*01"} 0
15761 TRA CALRLNNQGGKLIF TRAV9-2*01 TRAJ23*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CALRLNNQGGKLIF", "cdr3_old": "CALRLNNQGGKLIF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ23*01", "jStart": 6, "vCanonical": true, "vEnd": 3, "vFixType": "NoFixNeeded", "vId": "TRAV9-2*01"} 0
"""
path = PathBuilder.build(EnvironmentSettings.root_path +
"test/tmp/trcdist_encoder/")
with open(path + "receptors.tsv", "w") as file:
file.writelines(file_content)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path + "datasets/", "vdjdb")
params["is_repertoire"] = False
params["paired"] = True
params["result_path"] = path
params["path"] = path
params["sequence_file_size"] = 1
params["receptor_chains"] = "TRA_TRB"
params['organism'] = 'human'
dataset = VDJdbImport.import_dataset(params, "vdjdb_dataset")
encoder = TCRdistEncoder.build_object(dataset, **{"cores": 2})
encoded_dataset = encoder.encode(
dataset,
EncoderParams(f"{path}result/",
LabelConfiguration([Label("epitope")])))
self.assertTrue(encoded_dataset.encoded_data.examples.shape[0]
== encoded_dataset.encoded_data.examples.shape[1]
and encoded_dataset.encoded_data.examples.shape[0]
== dataset.get_example_count())
shutil.rmtree(path)
def test_encode(self):
path = EnvironmentSettings.tmp_test_path + "count_encoder/"
PathBuilder.build(path)
repertoires, metadata = RepertoireBuilder.build(
[["GGG", "III", "LLL", "MMM"],
["DDD", "EEE", "FFF", "III", "LLL", "MMM"], ["CCC", "FFF", "MMM"],
["AAA", "CCC", "EEE", "FFF", "LLL", "MMM"]],
labels={"l1": [True, True, False, False]},
path=path)
dataset = RepertoireDataset(repertoires=repertoires,
metadata_file=metadata,
identifier="1")
encoder = SequenceCountEncoder.build_object(
dataset, **{
"comparison_attributes": ["sequence_aas"],
"p_value_threshold": 0.4,
"sequence_batch_size": 4
})
label_config = LabelConfiguration(
[Label("l1", [True, False], positive_class=True)])
encoded_dataset = encoder.encode(
dataset, EncoderParams(result_path=path,
label_config=label_config))
test = encoded_dataset.encoded_data.examples
self.assertTrue(test[0] == 1)
self.assertTrue(test[1] == 1)
self.assertTrue(test[2] == 0)
self.assertTrue(test[3] == 0)
self.assertTrue("III" in encoded_dataset.encoded_data.feature_names)
shutil.rmtree(path)
def test_encode(self):
path = EnvironmentSettings.tmp_test_path + "distance_encoder/"
PathBuilder.build(path)
dataset = self.create_dataset(path)
enc = DistanceEncoder.build_object(
dataset, **{
"distance_metric": DistanceMetricType.JACCARD.name,
"attributes_to_match": ["sequence_aas"],
"sequence_batch_size": 20
})
enc.set_context({"dataset": dataset})
encoded = enc.encode(
dataset,
EncoderParams(result_path=path,
label_config=LabelConfiguration(
[Label("l1", [0, 1]),
Label("l2", [2, 3])]),
pool_size=4,
filename="dataset.pkl"))
self.assertEqual(8, encoded.encoded_data.examples.shape[0])
self.assertEqual(8, encoded.encoded_data.examples.shape[1])
self.assertEqual(1, encoded.encoded_data.examples.iloc[0, 0])
self.assertEqual(1, encoded.encoded_data.examples.iloc[1, 1])
self.assertEqual(1, encoded.encoded_data.examples.iloc[0, 4])
self.assertTrue(
np.array_equal([1, 0, 1, 0, 1, 0, 1, 0],
encoded.encoded_data.labels["l1"]))
self.assertTrue(
np.array_equal([2, 3, 2, 3, 2, 3, 3, 3],
encoded.encoded_data.labels["l2"]))
shutil.rmtree(path)
