def create_dataset(self):
path = Path(
os.path.relpath(EnvironmentSettings.root_path /
"test/tmp/immunemlapp/initial_dataset"))
PathBuilder.build(path)
repertoire_count = 30
repertoires, metadata = RepertoireBuilder.build(
[["AA", "AAAA", "AAAA", "AAA"] for i in range(repertoire_count)],
path, {
"CD": [
'yes' if i % 2 == 0 else 'no'
for i in range(repertoire_count)
],
"CMV": [
True if i % 2 == 1 else False
for i in range(repertoire_count)
]
}, [[{
"chain": "A" if i % 2 == 0 else "B",
"count": random.randint(2, 5)
} for i in range(4)] for j in range(repertoire_count)])
dataset = RepertoireDataset(repertoires=repertoires,
metadata_file=metadata,
labels={
"CD": [True, False],
"CMV": [True, False]
},
name="d1")
PickleExporter.export(dataset, path)
return path / "d1.iml_dataset"
def import_sequence_dataset(import_class, params, dataset_name: str):
PathBuilder.build(params.result_path)
filenames = ImportHelper.get_sequence_filenames(params.path, dataset_name)
file_index = 0
dataset_filenames = []
dataset_params = {}
items = None
for index, filename in enumerate(filenames):
new_items = ImportHelper.import_items(import_class, filename, params)
items = np.append(items, new_items) if items is not None else new_items
dataset_params = ImportHelper.extract_sequence_dataset_params(items, params)
while len(items) > params.sequence_file_size or (index == len(filenames) - 1 and len(items) > 0):
dataset_filenames.append(params.result_path / "batch_{}.pickle".format(file_index))
ImportHelper.store_sequence_items(dataset_filenames, items, params.sequence_file_size)
items = items[params.sequence_file_size:]
file_index += 1
init_kwargs = {"filenames": dataset_filenames, "file_size": params.sequence_file_size, "name": dataset_name, "labels": dataset_params}
dataset = ReceptorDataset(**init_kwargs) if params.paired else SequenceDataset(**init_kwargs)
PickleExporter.export(dataset, params.result_path)
return dataset
def make_random_dataset(self, path):
alphabet = EnvironmentSettings.get_sequence_alphabet()
sequences = [["".join([rn.choice(alphabet) for i in range(20)]) for i in range(100)] for i in range(40)]
repertoires, metadata = RepertoireBuilder.build(sequences, path, subject_ids=[i % 2 for i in range(len(sequences))])
dataset = RepertoireDataset(repertoires=repertoires, metadata_file=metadata)
PickleExporter.export(dataset, path)
def create_dummy_dataset(self, path):
repertoires, metadata = RepertoireBuilder.build([["AA"], ["CC"]], path, labels={"label1": ["val1", "val2"], "label2": ["val1", "val2"]})
dataset = RepertoireDataset(repertoires=repertoires, metadata_file=metadata)
dataset.name = "my_dataset"
PickleExporter.export(dataset, path)
return f"{dataset.name}.iml_dataset"
def import_dataset(params, dataset_name: str) -> ReceptorDataset:
generic_params = DatasetImportParams.build_object(**params)
filenames = ImportHelper.get_sequence_filenames(generic_params.path, dataset_name)
PathBuilder.build(generic_params.result_path, warn_if_exists=True)
dataset = SingleLineReceptorImport._import_from_files(filenames, generic_params)
dataset.name = dataset_name
dataset.labels = ImportHelper.extract_sequence_dataset_params(params=generic_params)
PickleExporter.export(dataset, generic_params.result_path)
return dataset
def test_load_receptors(self):
path = EnvironmentSettings.tmp_test_path / "pickle_import_receptors/"
PathBuilder.build(path)
dataset = RandomDatasetGenerator.generate_receptor_dataset(
10, {2: 1}, {3: 1}, {}, path)
dataset.name = "d1"
PickleExporter.export(dataset, path)
receptor_dataset = PickleImport.import_dataset(
{"path": path / "d1.iml_dataset"}, "dataset_name")
self.assertEqual(10, len(list(receptor_dataset.get_data())))
shutil.rmtree(path)
def test_export_receptor_dataset(self):
path = EnvironmentSettings.tmp_test_path / "pickleexporter_receptor/"
PathBuilder.build(path)
dataset = RandomDatasetGenerator.generate_receptor_dataset(
10, {2: 1}, {3: 1}, {}, path)
dataset.name = "d1"
PickleExporter.export(dataset, path)
with open(path / f"{dataset.name}.iml_dataset", "rb") as file:
dataset2 = pickle.load(file)
self.assertTrue(isinstance(dataset2, ReceptorDataset))
self.assertEqual(10, dataset2.get_example_count())
shutil.rmtree(path)
def import_repertoire_dataset(import_class, params: DatasetImportParams,
dataset_name: str) -> RepertoireDataset:
"""
Function to create a dataset from the metadata and a list of repertoire files and exports dataset pickle file
Arguments:
import_class: class to use for import
params: instance of DatasetImportParams class which includes information on path, columns, result path etc.
dataset_name: user-defined name of the dataset
Returns:
RepertoireDataset object that was created
"""
metadata = pd.read_csv(params.metadata_file, ",")
ParameterValidator.assert_keys_present(
metadata.columns.tolist(), ["filename"], ImportHelper.__name__,
f'{dataset_name}: params: metadata_file')
PathBuilder.build(params.result_path / "repertoires/")
arguments = [(import_class, row, params)
for index, row in metadata.iterrows()]
with Pool(params.number_of_processes) as pool:
repertoires = pool.starmap(ImportHelper.load_repertoire_as_object,
arguments)
new_metadata_file = ImportHelper.make_new_metadata_file(
repertoires, metadata, params.result_path, dataset_name)
potential_labels = list(set(metadata.columns.tolist()) - {"filename"})
dataset = RepertoireDataset(labels={
key: list(set(metadata[key].values.tolist()))
for key in potential_labels
},
repertoires=repertoires,
metadata_file=new_metadata_file,
name=dataset_name)
PickleExporter.export(dataset, params.result_path)
return dataset
def test_export(self):
path = EnvironmentSettings.tmp_test_path / "pickleexporter/"
PathBuilder.build(path)
repertoires, metadata = RepertoireBuilder.build([["AA"], ["CC"]], path)
dataset = RepertoireDataset(repertoires=repertoires,
metadata_file=metadata)
PickleExporter.export(
dataset, EnvironmentSettings.tmp_test_path / "pickleexporter/")
with open(
EnvironmentSettings.tmp_test_path /
f"pickleexporter/{dataset.name}.iml_dataset", "rb") as file:
dataset2 = pickle.load(file)
shutil.rmtree(EnvironmentSettings.tmp_test_path / "pickleexporter/")
self.assertTrue(isinstance(dataset2, RepertoireDataset))
self.assertEqual(2, len(dataset2.get_data()))
self.assertEqual("rep_0",
dataset2.get_data()[0].metadata["subject_id"])
def prepare_dataset(self, path):
PathBuilder.build(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,
labels={
"l1": [1, 2],
"l2": [0, 1]
},
name="dataset1")
PickleExporter.export(dataset, path)
def test_run(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path /
"api_galaxy_yaml_tool/")
result_path = path / "result/"
dataset = RandomDatasetGenerator.generate_repertoire_dataset(
10, {10: 1}, {12: 1}, {}, result_path)
dataset.name = "d1"
PickleExporter.export(dataset, result_path)
specs = {
"definitions": {
"datasets": {
"new_d1": {
"format": "Pickle",
"params": {
"metadata_file":
str(result_path / "d1_metadata.csv")
}
},
"d2": {
"format": "RandomRepertoireDataset",
"params": {
"repertoire_count": 50,
"sequence_length_probabilities": {
10: 1
},
'sequence_count_probabilities': {
10: 1
},
'labels': {
"CD": {
True: 0.5,
False: 0.5
}
}
}
}
},
"encodings": {
"e1": {
"Word2Vec": {
"k": 3,
"model_type": "sequence",
"vector_size": 8,
}
},
"e2": {
"Word2Vec": {
"k": 3,
"model_type": "sequence",
"vector_size": 10,
}
},
},
"ml_methods": {
"simpleLR": {
"LogisticRegression": {
"penalty": "l1"
},
"model_selection_cv": False,
"model_selection_n_folds": -1,
}
},
},
"instructions": {
"inst1": {
"type": "DatasetExport",
"datasets": ["new_d1", 'd2'],
"export_formats": ["AIRR"]
},
"inst2": {
"type":
"TrainMLModel",
"settings": [{
"encoding": "e1",
"ml_method": "simpleLR"
}, {
"encoding": "e2",
"ml_method": "simpleLR"
}],
"assessment": {
"split_strategy": "random",
"split_count": 1,
"training_percentage": 0.7
},
"selection": {
"split_strategy": "random",
"split_count": 2,
"training_percentage": 0.7
},
"labels": ["CD"],
"dataset":
"d2",
"strategy":
"GridSearch",
"metrics": ["accuracy", "auc"],
"reports": [],
"number_of_processes":
10,
"optimization_metric":
"accuracy",
'refit_optimal_model':
False,
"store_encoded_data":
False
}
}
}
specs_path = path / "specs.yaml"
with open(specs_path, "w") as file:
yaml.dump(specs, file)
run_immuneML(
Namespace(
**{
"specification_path": specs_path,
"result_path": result_path / 'result/',
'tool': "GalaxyYamlTool"
}))
self.assertTrue(
os.path.exists(result_path / "result/inst1/new_d1/AIRR"))
self.assertTrue(os.path.exists(result_path / "result/inst1/d2/AIRR"))
self.assertTrue(os.path.exists(result_path / "result/d2"))
shutil.rmtree(path)
def store(encoded_dataset, params: EncoderParams):
PickleExporter.export(encoded_dataset, params.result_path)
def test_parse_yaml_file(self):
path = EnvironmentSettings.root_path / "test/tmp/parser/"
dataset = RepertoireDataset(repertoires=RepertoireBuilder.build(
[["AAA", "CCC"], ["TTTT"]], path, {"default": [1, 2]})[0],
labels={"default": [1, 2]})
PickleExporter.export(dataset, path)
spec = {
"definitions": {
"datasets": {
"d1": {
"format": "Pickle",
"params": {
"path": str(path / f"{dataset.name}.iml_dataset"),
}
}
},
"encodings": {
"a1": {
"Word2Vec": {
"k": 3,
"model_type": "sequence",
"vector_size": 8,
}
},
"a2": "Word2Vec"
},
"ml_methods": {
"simpleLR": {
"LogisticRegression": {
"penalty": "l1"
},
"model_selection_cv": False,
"model_selection_n_folds": -1,
},
"simpleLR2": "LogisticRegression"
},
"reports": {
"rep1": "SequenceLengthDistribution"
}
},
"instructions": {}
}
PathBuilder.build(path)
specs_filename = path / "tmp_yaml_spec.yaml"
with specs_filename.open("w") as file:
yaml.dump(spec, file, default_flow_style=False)
symbol_table, _ = ImmuneMLParser.parse_yaml_file(specs_filename,
result_path=path)
self.assertTrue(
all([
symbol_table.contains(key)
for key in ["simpleLR", "rep1", "a1", "d1"]
]))
self.assertTrue(isinstance(symbol_table.get("d1"), RepertoireDataset))
with self.assertRaises(YAMLError):
with specs_filename.open("r") as file:
specs_text = file.readlines()
specs_text[0] = " definitions:"
with specs_filename.open("w") as file:
file.writelines(specs_text)
ImmuneMLParser.parse_yaml_file(specs_filename, result_path=path)
shutil.rmtree(path)
def test_encoding(self):
path = EnvironmentSettings.tmp_test_path / "integration_test_emerson_encoding/"
PathBuilder.build(path)
ref_path = path / "reference.csv"
pd.DataFrame({
"sequence_aas": ["GGG", "III", "TTT", "EFEF"],
"v_alleles":
["TRBV6-1*01", "TRBV6-1*01", "TRBV6-1*01", "TRBV6-1*01"],
'j_alleles': ["TRBJ2-7", "TRBJ2-7", "TRBJ2-7", "TRBJ2-7"]
}).to_csv(ref_path, index=False)
repertoires, metadata = RepertoireBuilder.build(
[["GGG", "III", "LLL", "MMM"],
["DDD", "EEE", "FFF", "III", "LLL", "MMM"], ["CCC", "FFF", "MMM"],
["AAA", "CCC", "EEE", "FFF", "LLL", "MMM"],
["GGG", "III", "LLL", "MMM"],
["DDD", "EEE", "FFF", "III", "LLL", "MMM"], ["CCC", "FFF", "MMM"],
["AAA", "CCC", "EEE", "FFF", "LLL", "MMM"],
["GGG", "III", "LLL", "MMM"],
["DDD", "EEE", "FFF", "III", "LLL", "MMM"], ["CCC", "FFF", "MMM"],
["AAA", "CCC", "EEE", "FFF", "LLL", "MMM"],
["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, True, True, False, False, True,
True, False, False, True, True, False, False
]
},
path=path)
dataset = RepertoireDataset(repertoires=repertoires,
metadata_file=metadata,
labels={"l1": [True, False]})
PickleExporter.export(dataset, path)
specs = {
"definitions": {
"datasets": {
"d1": {
"format": "Pickle",
"params": {
"path": str(path / f"{dataset.name}.iml_dataset"),
}
}
},
"encodings": {
"e1": {
"SequenceAbundance": {
'comparison_attributes':
["sequence_aas", "v_alleles", "j_alleles"]
}
}
},
"ml_methods": {
"knn": {
"KNN": {
"n_neighbors": 1
},
}
},
"reports": {
"r1": {
"ReferenceSequenceOverlap": {
"reference_path":
str(ref_path),
'comparison_attributes':
["sequence_aas", "v_alleles", "j_alleles"]
}
}
}
},
"instructions": {
"inst1": {
"type": "TrainMLModel",
"settings": [{
"encoding": "e1",
"ml_method": "knn"
}],
"assessment": {
"split_strategy": "random",
"split_count": 1,
"training_percentage": 0.7,
"reports": {}
},
"selection": {
"split_strategy": "random",
"split_count": 1,
"training_percentage": 0.7,
},
"labels": [{
"l1": {
"positive_class": True
}
}],
"dataset": "d1",
"strategy": "GridSearch",
"metrics": ["accuracy"],
"number_of_processes": 2,
"reports": ["r1"],
"optimization_metric": "balanced_accuracy",
"refit_optimal_model": True,
"store_encoded_data": False
}
}
}
specs_file = path / "specs.yaml"
with open(specs_file, "w") as file:
yaml.dump(specs, file)
app = ImmuneMLApp(specs_file, path / "result")
app.run()
shutil.rmtree(path)
def test_generate(self):
path = EnvironmentSettings.tmp_test_path / "disease_assoc_seq_cv/"
PathBuilder.build(path)
repertoires, metadata = RepertoireBuilder.build(
[["GGG", "III", "LLL", "MMM"], ["DDD", "EEE", "FFF"],
["GGG", "III", "LLL", "MMM"], ["DDD", "EEE", "FFF"],
["GGG", "III", "LLL", "MMM"], ["DDD", "EEE", "FFF"],
["GGG", "III", "LLL", "MMM"], ["DDD", "EEE", "FFF"],
["GGG", "III", "LLL", "MMM"], ["DDD", "EEE", "FFF"],
["GGG", "III", "LLL", "MMM"], ["DDD", "EEE", "FFF"],
["GGG", "III", "LLL", "MMM"], ["DDD", "EEE", "FFF"]],
labels={
"l1": [
True, False, True, False, True, False, True, False, True,
False, True, False, True, False
]
},
path=path)
dataset = RepertoireDataset(repertoires=repertoires,
metadata_file=metadata,
labels={"l1": [True, False]})
PickleExporter.export(dataset, path)
specs = {
"definitions": {
"datasets": {
"d1": {
"format": "Pickle",
"params": {
"path": str(path / f"{dataset.name}.iml_dataset"),
}
}
},
"encodings": {
"e1": {
"SequenceAbundance": {
'p_value_threshold': 0.5
}
}
},
"ml_methods": {
"knn": {
"KNN": {
"n_neighbors": 1
},
}
},
"reports": {
"r1": {
"DiseaseAssociatedSequenceCVOverlap": {
"compare_in_selection": True,
"compare_in_assessment": True
}
}
}
},
"instructions": {
"inst1": {
"type": "TrainMLModel",
"settings": [{
"encoding": "e1",
"ml_method": "knn"
}],
"assessment": {
"split_strategy": "random",
"split_count": 1,
"training_percentage": 0.5,
"reports": {}
},
"selection": {
"split_strategy": "random",
"split_count": 1,
"training_percentage": 0.5,
},
"labels": [{
"l1": {
"positive_class": True
}
}],
"dataset": "d1",
"strategy": "GridSearch",
"metrics": ["accuracy"],
"number_of_processes": 2,
"reports": ["r1"],
"optimization_metric": "balanced_accuracy",
"refit_optimal_model": True,
"store_encoded_data": False
}
}
}
specs_file = path / "specs.yaml"
with open(specs_file, "w") as file:
yaml.dump(specs, file)
app = ImmuneMLApp(specs_file, path / "result/")
state = app.run()[0]
self.assertEqual(1, len(state.report_results))
self.assertTrue(len(state.report_results[0].output_figures) > 0)
self.assertTrue(len(state.report_results[0].output_tables) > 0)
for fig in state.report_results[0].output_figures:
self.assertTrue(os.path.isfile(fig.path))
for table in state.report_results[0].output_tables:
self.assertTrue(os.path.isfile(table.path))
shutil.rmtree(path)
