def test_sequence_dataset(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path /
"integration_dataset_gen_html_sequence/")
dataset_path = path / "sequence_dataset/"
specs = {
"definitions": {
"datasets": {
"sequencedataset": {
"format": "RandomSequenceDataset",
"params": {
"sequence_count": 10,
"length_probabilities": {
10: 1
},
"labels": {
"epitope_a": {
True: 0.5,
False: 0.5
},
"epitope_b": {
True: 0.5,
False: 0.5
}
},
"result_path": str(dataset_path)
}
}
}
},
"instructions": {
"instr1": {
"type": "DatasetExport",
"export_formats": ["Pickle", "AIRR"],
"datasets": ["sequencedataset"]
}
},
"output": {
"format": "HTML"
}
}
specs_path = path / "specs.yaml"
with open(specs_path, "w") as file:
yaml.dump(specs, file)
app = ImmuneMLApp(specs_path, path / "result/")
app.run()
shutil.rmtree(path)
def test_dataset_generation(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path /
"cv_split_variant/")
repertoire_specs = self.build_specs(path)
specs_filename = path / "specs.yaml"
with open(specs_filename, "w") as file:
yaml.dump(repertoire_specs, file)
app = ImmuneMLApp(specs_filename, path / "result/")
app.run()
shutil.rmtree(path)
def test_subsampling(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path /
"subsampling_workflow/")
repertoire_specs = self.build_specs(path)
specs_filename = path / "specs.yaml"
with open(specs_filename, "w") as file:
yaml.dump(repertoire_specs, file)
app = ImmuneMLApp(specs_filename, path / "result/")
app.run()
shutil.rmtree(path)
def _run(self):
PathBuilder.build(self.result_path)
self._prepare_specs()
app = ImmuneMLApp(self.yaml_path, self.result_path)
app.run()
model_locations = list(self.result_path.glob(f"{self.instruction_name}/optimal_*/zip/*.zip"))
model_export_path = PathBuilder.build(self.result_path / 'exported_models/')
for model_location in model_locations:
shutil.copyfile(model_location, model_export_path / model_location.name)
logging.info(f"{GalaxyTrainMLModel.__name__}: immuneML has finished and the trained models were exported.")
def run(self, result_path: str):
result_path = self.build_path(result_path)
self._simulate_dataset_with_signals(result_path / "synthetic_dataset")
print("immuneML quickstart: training a machine learning model...")
specs_file = self.create_specfication(result_path /
"machine_learning_analysis")
app = ImmuneMLApp(specs_file,
result_path / "machine_learning_analysis/result")
app.run()
print(
"immuneML quickstart: finished training a machine learning model.")
def run(self, result_path: str):
result_path = self.build_path(result_path)
logging.basicConfig(filename=Path(result_path) / "log.txt", level=logging.ERROR, format='%(asctime)s %(levelname)s: %(message)s')
warnings.showwarning = lambda message, category, filename, lineno, file=None, line=None: logging.warning(message)
self._simulate_dataset_with_signals(result_path / "synthetic_dataset")
print("immuneML quickstart: training a machine learning model...")
specs_file = self.create_specfication(result_path / "machine_learning_analysis")
app = ImmuneMLApp(specs_file, result_path / "machine_learning_analysis/result")
app.run()
print("immuneML quickstart: finished training a machine learning model.")
def _run(self):
PathBuilder.build(self.result_path)
self._update_specs()
state = ImmuneMLApp(self.yaml_path, self.result_path).run()[0]
shutil.copytree(
list(list(state.paths.values())[0].values())[0],
self.result_path / "result/")
print("Exported dataset.")
def _run(self):
PathBuilder.build(self.result_path)
self._check_specs()
state = ImmuneMLApp(self.yaml_path, self.result_path).run()[0]
if os.path.relpath(state.predictions_path) != os.path.relpath(
self.result_path / "predictions.csv"):
shutil.copy(state.predictions_path,
self.result_path / "predictions.csv")
print("Applied ML model to the dataset, predictions are available.")
def test_simulation(self):
path = EnvironmentSettings.tmp_test_path / "integration_simulation/"
self.prepare_dataset(path)
specs_path = self.prepare_specs(path)
PathBuilder.build(path / "result/")
app = ImmuneMLApp(specification_path=specs_path,
result_path=path / "result/")
app.run()
self.assertTrue(os.path.isfile(path / "result/inst1/metadata.csv"))
metadata_df = pd.read_csv(path / "result/inst1/metadata.csv",
comment=Constants.COMMENT_SIGN)
self.assertTrue("signal1" in metadata_df.columns)
self.assertEqual(17, sum(metadata_df["signal1"]))
self.assertTrue(os.path.isfile(path / "result/index.html"))
self.assertTrue(
os.path.isfile(
path / "result/inst1/exported_dataset/pickle/d1.iml_dataset"))
shutil.rmtree(path)
def test_ml(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path /
"integration_ml/")
specs_path = self.prepare_specs(path)
PathBuilder.build(path / "result_export/")
app = ImmuneMLApp(specification_path=Path(specs_path),
result_path=path / "result_export/")
states = app.run()
self.assertTrue(os.path.isfile(path / "result_export/index.html"))
specs_path = self.prepare_import_specs(path)
app = ImmuneMLApp(Path(specs_path), path / 'result_import/')
result_path = app.run()
self.assertTrue(os.path.isfile(path / "result_import/index.html"))
shutil.rmtree(path)
def run_tool(yaml_path, result_path):
PathBuilder.build(result_path)
app = ImmuneMLApp(yaml_path, result_path)
app.run()
def test_simulation_receptors(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path /
"integration_simulation_receptor/")
specs = {
"definitions": {
"datasets": {
"d1": {
"format": "RandomReceptorDataset",
"params": {
"receptor_count": 100,
"chain_1_length_probabilities": {
10: 1
},
"chain_2_length_probabilities": {
10: 1
},
"result_path": str(path / "dataset/"),
"labels": {}
}
},
},
"motifs": {
"motif1": {
"seed_chain1": "CC/C",
"name_chain1": "ALPHA",
"name_chain2": "BETA",
"seed_chain2": "F/FF",
"instantiation": {
"GappedKmer": {
"max_gap": 1,
"alphabet_weights": None,
"position_weights": None
},
}
},
"motif2": {
"seed_chain1": "CCC",
"name_chain1": "ALPHA",
"name_chain2": "BETA",
"seed_chain2": "FFF",
"instantiation": "GappedKmer"
}
},
"signals": {
"signal1": {
"motifs": ["motif1", "motif2"],
"implanting": "Receptor",
"sequence_position_weights": None
},
"signal2": {
"motifs": ["motif1"],
"implanting": "Receptor",
"sequence_position_weights": None
}
},
"simulations": {
"sim1": {
"var1": {
"signals": ["signal1"],
"dataset_implanting_rate": 0.5
},
"var2": {
"signals": ["signal2"],
"dataset_implanting_rate": 0.5
}
}
}
},
"instructions": {
"inst1": {
"type": "Simulation",
"dataset": "d1",
"simulation": "sim1",
"export_formats": ["Pickle"]
}
},
"output": {
"format": "HTML"
}
}
with open(path / "specs.yaml", "w") as file:
yaml.dump(specs, file)
app = ImmuneMLApp(path / "specs.yaml", path / "result/")
app.run()
self.assertTrue(os.path.isfile(path / "result/index.html"))
self.assertTrue(
os.path.isfile(
path / "result/inst1/exported_dataset/pickle/d1.iml_dataset"))
dataset = PickleImport.import_dataset(
{
"path":
path / "result/inst1/exported_dataset/pickle/d1.iml_dataset"
}, "d1")
self.assertEqual(100, dataset.get_example_count())
self.assertEqual(
100,
len([
receptor for receptor in dataset.get_data()
if "signal1" in receptor.metadata
]))
self.assertEqual(
50,
len([
receptor for receptor in dataset.get_data()
if receptor.metadata["signal1"]
]))
self.assertEqual(
100,
len([
receptor for receptor in dataset.get_data()
if "signal2" in receptor.metadata
]))
self.assertEqual(
50,
len([
receptor for receptor in dataset.get_data()
if receptor.metadata["signal2"]
]))
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]})
ImmuneMLExporter.export(dataset, path)
specs = {
"definitions": {
"datasets": {
"d1": {
"format": "ImmuneML",
"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,
}
}
}
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(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path /
"integration_receptor_cnn_workflow/")
specs = {
"definitions": {
"datasets": {
"d1": {
"format": "RandomReceptorDataset",
"params": {
"result_path": str(path / "generated_dataset/"),
"receptor_count": 500,
"chain_1_length_probabilities": {
5: 1.
},
"chain_2_length_probabilities": {
6: 1.
},
"labels": {
"cmv_epitope": {
True: 0.5,
False: 0.5
}
}
}
}
},
"encodings": {
"enc1": {
"OneHot": {
"use_positional_info": True
}
}
},
"ml_methods": {
"cnn": {
"ReceptorCNN": {
"iteration_count": 1000,
"evaluate_at": 10,
"batch_size": 100,
"number_of_threads": 4
}
}
}
},
"instructions": {
"instr1": {
"type": "TrainMLModel",
"settings": [{
"encoding": "enc1",
"ml_method": "cnn"
}],
"assessment": {
"split_strategy": "random",
"split_count": 1,
"training_percentage": 0.7,
},
"selection": {
"split_strategy": "random",
"split_count": 1,
"training_percentage": 1,
},
"labels": ["cmv_epitope"],
"dataset": "d1",
"strategy": "GridSearch",
"metrics": ["accuracy"],
"number_of_processes": 4,
"reports": None,
"optimization_metric": "balanced_accuracy",
"refit_optimal_model": False,
}
}
}
with open(path / "specs.yaml", "w") as file:
yaml.dump(specs, file)
app = ImmuneMLApp(path / "specs.yaml", 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)
