def build_object(cls, **kwargs):
location = "Coefficients"
coefs_to_plot = [coef.upper() for coef in kwargs["coefs_to_plot"]]
name = kwargs["name"] if "name" in kwargs else None
ParameterValidator.assert_all_in_valid_list(coefs_to_plot, [item.name.upper() for item in CoefficientPlottingSetting], location,
"coefs_to_plot")
if CoefficientPlottingSetting.CUTOFF.name in coefs_to_plot:
cutoff = kwargs["cutoff"]
ParameterValidator.assert_type_and_value(cutoff, list, location, "cutoff")
ParameterValidator.assert_all_type_and_value(cutoff, Number, location, "cutoff", min_inclusive=1e-15)
else:
cutoff = []
if CoefficientPlottingSetting.N_LARGEST.name in coefs_to_plot:
n_largest = kwargs["n_largest"]
ParameterValidator.assert_type_and_value(n_largest, list, location, "n_largest")
ParameterValidator.assert_all_type_and_value(n_largest, int, location, "n_largest", min_inclusive=1)
else:
n_largest = []
coefs = CoefficientPlottingSettingList()
for keyword in coefs_to_plot:
coefs.append(CoefficientPlottingSetting[keyword.upper()])
return Coefficients(coefs, cutoff, n_largest, name)
def parse_object(specs,
valid_class_names: list,
class_name_ending: str,
class_path: str,
location: str,
key: str,
builder: bool = False,
return_params_dict: bool = False):
class_name = ObjectParser.get_class_name(specs, valid_class_names,
class_name_ending, location,
key)
ParameterValidator.assert_in_valid_list(class_name, valid_class_names,
location, key)
cls = ReflectionHandler.get_class_by_name(
f"{class_name}{class_name_ending}", class_path)
params = ObjectParser.get_all_params(specs, class_path, class_name,
key)
try:
if "name" not in inspect.signature(cls.__init__).parameters.keys():
del params["name"]
obj = cls.build_object(
**params) if builder and hasattr(cls, "build_object") else cls(
**params)
except TypeError as err:
raise AssertionError(
f"{location}: invalid parameter {err.args[0]} when specifying parameters in {specs} "
f"under key {key}. Valid parameter names are: "
f"{[name for name in inspect.signature(cls.__init__).parameters.keys()]}"
)
return (obj, {class_name: params}) if return_params_dict else obj
def _prepare_specs(self):
with open(self.yaml_path, "r") as file:
specs = yaml.safe_load(file)
ParameterValidator.assert_keys_present(specs.keys(),
["definitions", "instructions"],
GalaxyTrainMLModel.__name__,
"YAML specification")
ParameterValidator.assert_all_in_valid_list(
specs.keys(), ["definitions", "instructions", "output"],
GalaxyTrainMLModel.__name__, "YAML specification")
ParameterValidator.assert_type_and_value(specs["instructions"], dict,
GalaxyTrainMLModel.__name__,
"instructions")
assert len(list(specs["instructions"].keys())) == 1, f"{GalaxyTrainMLModel.__name__}: one instruction has to be specified under " \
f"`instructions`, got the following instead: {list(specs['instructions'].keys())}."
self.instruction_name = list(specs["instructions"].keys())[0]
ParameterValidator.assert_type_and_value(
specs['instructions'][self.instruction_name], dict,
GalaxyTrainMLModel.__name__, self.instruction_name)
ParameterValidator.assert_keys_present(
specs['instructions'][self.instruction_name].keys(), ['type'],
GalaxyTrainMLModel.__name__, self.instruction_name)
assert specs['instructions'][self.instruction_name]['type'] == TrainMLModelInstruction.__name__[:-11], \
f"{GalaxyTrainMLModel.__name__}: instruction `type` under {self.instruction_name} has to be {TrainMLModelInstruction.__name__[:-11]} " \
f"for this tool."
Util.check_paths(specs, GalaxyTrainMLModel.__name__)
Util.update_result_paths(specs, self.result_path, self.yaml_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(params={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 _prepare_report_config(self, instruction_key, instruction, split_key,
symbol_table):
if "reports" in instruction[split_key]:
location = f"{instruction_key}/{split_key}/reports"
report_types = list(signature(ReportConfig).parameters.keys())
ParameterValidator.assert_all_in_valid_list(
instruction[split_key]["reports"].keys(), report_types,
location, "reports")
for report_type in instruction[split_key]["reports"]:
ParameterValidator.assert_type_and_value(
instruction[split_key]["reports"][report_type], list,
f"{location}/{report_type}", report_type)
report_config_input = {
report_type: {
report_id: symbol_table.get(report_id)
for report_id in instruction[split_key]["reports"]
[report_type]
}
for report_type in instruction[split_key]["reports"]
}
else:
report_config_input = {}
return report_config_input
def _parse_ml_method(ml_method_id: str, ml_specification) -> tuple:
valid_class_values = ReflectionHandler.all_nonabstract_subclass_basic_names(MLMethod, "", "ml_methods/")
if type(ml_specification) is str:
ml_specification = {ml_specification: {}}
ml_specification = {**DefaultParamsLoader.load("ml_methods/", "MLMethod"), **ml_specification}
ml_specification_keys = list(ml_specification.keys())
ParameterValidator.assert_all_in_valid_list(list(ml_specification_keys), ["model_selection_cv", "model_selection_n_folds"] +
valid_class_values, "MLParser", ml_method_id)
non_default_keys = [key for key in ml_specification.keys() if key not in ["model_selection_cv", "model_selection_n_folds"]]
assert len(ml_specification_keys) == 3, f"MLParser: ML method {ml_method_id} was not correctly specified. Expected at least 1 key " \
f"(ML method name), got {len(ml_specification_keys) - 2} instead: " \
f"{str([key for key in non_default_keys])[1:-1]}."
ml_method_class_name = non_default_keys[0]
ml_method_class = ReflectionHandler.get_class_by_name(ml_method_class_name, "ml_methods/")
ml_specification[ml_method_class_name] = {**DefaultParamsLoader.load("ml_methods/", ml_method_class_name, log_if_missing=False),
**ml_specification[ml_method_class_name]}
method, params = MLParser.create_method_instance(ml_specification, ml_method_class, ml_method_id)
ml_specification[ml_method_class_name] = params
method.name = ml_method_id
return method, ml_specification
def import_dataset(params, name: str) -> SequenceDataset:
"""
Returns randomly generated receptor dataset according to the parameters;
YAML specification:
result_path: path/where/to/store/results/
sequence_count: 100 # number of random sequences to generate
chain_1_length_probabilities:
14: 0.8 # 80% of all generated sequences for all sequences will have length 14
15: 0.2 # 20% of all generated sequences across all sequences will have length 15
labels:
epitope1: # label name
True: 0.5 # 50% of the sequences will have class True
False: 0.5 # 50% of the sequences will have class False
epitope2: # next label with classes that will be assigned to sequences independently of the previous label or other parameters
1: 0.3 # 30% of the generated sequences will have class 1
0: 0.7 # 70% of the generated sequences will have class 0
"""
valid_keys = [
"sequence_count", "length_probabilities", "labels", "result_path"
]
ParameterValidator.assert_all_in_valid_list(
list(params.keys()), valid_keys, "RandomSequenceDatasetImport",
"params")
return RandomDatasetGenerator.generate_sequence_dataset(
sequence_count=params["sequence_count"],
length_probabilities=params["length_probabilities"],
labels=params["labels"],
path=params["result_path"])
def _prepare_parameters(reference: dict,
max_edit_distances: dict,
name: str = None):
location = "MatchedReceptorsEncoder"
legal_chains = [
chain
for receptor in (TCABReceptor(), TCGDReceptor(), BCReceptor())
for chain in receptor.get_chains()
]
if type(max_edit_distances) is int:
max_edit_distances = {
chain: max_edit_distances
for chain in legal_chains
}
elif type(max_edit_distances) is dict:
ParameterValidator.assert_keys(max_edit_distances.keys(),
legal_chains,
location,
"max_edit_distances",
exclusive=False)
else:
ParameterValidator.assert_type_and_value(max_edit_distances, dict,
location,
'max_edit_distances')
reference_receptors = MatchedReferenceUtil.prepare_reference(
reference, location=location, paired=True)
return {
"reference_receptors": reference_receptors,
"max_edit_distances": max_edit_distances,
"name": name
}
def import_dataset(params: dict, dataset_name: str) -> RepertoireDataset:
valid_keys = ["result_path", "repertoire_count", "sequence_count_probabilities", "sequence_length_probabilities", "labels"]
ParameterValidator.assert_all_in_valid_list(list(params.keys()), valid_keys, "RandomRepertoireDatasetImport", "params")
return RandomDatasetGenerator.generate_repertoire_dataset(repertoire_count=params["repertoire_count"],
sequence_count_probabilities=params["sequence_count_probabilities"],
sequence_length_probabilities=params["sequence_length_probabilities"],
labels=params["labels"],
path=params["result_path"])
def _check_specs(self, workflow_specification):
location = 'MultiDatasetBenchmarkTool'
ParameterValidator.assert_keys(
workflow_specification.keys(),
['definitions', 'instructions', 'output'], location,
'YAML specification')
self._check_dataset_specs(workflow_specification, location)
self._check_instruction_specs(workflow_specification, location)
def _check_dataset(self, specs):
ParameterValidator.assert_keys_present(specs["definitions"].keys(), ['datasets'], DatasetGenerationTool.__name__, 'definitions')
assert len(specs['definitions']['datasets'].keys()) == 1, \
f"{DatasetGenerationTool.__name__}: only one dataset can be defined with this Galaxy tool, got these " \
f"instead: {list(specs['definitions']['datasets'].keys())}."
assert len(specs['instructions'].keys()) == 1, \
f"{DatasetGenerationTool.__name__}: only one instruction of type DatasetExport can be defined with this Galaxy tool, got these " \
f"instructions instead: {list(specs['instructions'].keys())}."
def _check_label_format(self, labels: list, instruction_key: str):
ParameterValidator.assert_type_and_value(labels, list,
TrainMLModelParser.__name__,
f'{instruction_key}/labels')
assert all(isinstance(label, str) or isinstance(label, dict) for label in labels), \
f"{TrainMLModelParser.__name__}: labels under {instruction_key} were not defined properly. The list of labels has to either be a list of " \
f"label names, or there can be a parameter 'positive_class' defined under the label name."
assert all(len(list(label.keys())) == 1 and isinstance(list(label.values())[0], dict) and 'positive_class' in list(label.values())[0]
and len(list(list(label.values())[0].keys())) == 1 for label in [l for l in labels if isinstance(l, dict)]), \
f"{TrainMLModelParser.__name__}: labels that are specified by more than label name, can include only one parameter called 'positive_class'."
def parse(specs: dict, symbol_table: SymbolTable) -> dict:
if "output" in specs:
ParameterValidator.assert_keys(specs["output"], ["format"],
"OutputParser", "output")
ParameterValidator.assert_in_valid_list(specs["output"]["format"],
["HTML"], "OutputParser",
"format")
else:
specs["output"] = {"format": "HTML"}
symbol_table.add("output", SymbolType.OUTPUT, specs["output"])
return specs["output"]
def _prepare_parameters(max_edit_distance: int, reference: dict, name: str = None):
location = "MatchedSequencesEncoder"
ParameterValidator.assert_type_and_value(max_edit_distance, int, location, "max_edit_distance", min_inclusive=0)
reference_sequences = MatchedReferenceUtil.prepare_reference(reference_params=reference, location=location, paired=False)
return {
"max_edit_distance": max_edit_distance,
"reference_sequences": reference_sequences,
"name": name
}
def _check_instruction(self, specs):
instruction_name = Util.check_instruction_type(specs, DatasetGenerationTool.__name__, DatasetExportInstruction.__name__[:-11])
for key in ['datasets', 'export_formats']:
ParameterValidator.assert_keys_present(list(specs['instructions'][instruction_name].keys()), [key], DatasetGenerationTool.__name__,
instruction_name)
ParameterValidator.assert_type_and_value(specs["instructions"][instruction_name][key], list, DatasetGenerationTool.__name__,
f"{instruction_name}/{key}")
assert len(specs['instructions'][instruction_name][key]) == 1, \
f"{DatasetGenerationTool.__name__}: this tool accepts only one item under {key}, got {specs['instructions'][instruction_name][key]} " \
f"instead."
def update_specs(self):
with open(self.yaml_path, 'r') as file:
specs = yaml.safe_load(file)
ParameterValidator.assert_keys_present(specs.keys(), ["definitions", "instructions"], DatasetGenerationTool.__name__, "YAML specification")
ParameterValidator.assert_all_in_valid_list(specs.keys(), ["definitions", "instructions", "output"], DatasetGenerationTool.__name__, "YAML specification")
self._check_dataset(specs)
self._check_instruction(specs)
Util.check_paths(specs, DatasetGenerationTool.__name__)
Util.update_result_paths(specs, self.result_path, self.yaml_path)
def _prepare_reports(self, reports: list,
symbol_table: SymbolTable) -> dict:
if reports is not None:
report_objects = {
report_id: symbol_table.get(report_id)
for report_id in reports
}
ParameterValidator.assert_all_type_and_value(
report_objects.values(), TrainMLModelReport,
TrainMLModelParser.__name__, 'reports')
return report_objects
else:
return {}
def check_instruction_type(specs: dict, tool_name,
expected_instruction) -> str:
ParameterValidator.assert_keys_present(list(specs.keys()),
['definitions', 'instructions'],
tool_name, "YAML specification")
assert len(list(specs['instructions'].keys())) == 1, f"{tool_name}: multiple instructions were given " \
f"({str(list(specs['instructions'].keys()))[1:-1]}), but only one instruction of type " \
f"{expected_instruction} should be specified."
instruction_name = list(specs['instructions'].keys())[0]
instruction_type = specs['instructions'][instruction_name]['type']
assert instruction_type == expected_instruction, \
f"{tool_name}: instruction type has to be '{expected_instruction}', got {instruction_type} instead."
return instruction_name
def __init__(self, percentage: float, show_warnings: bool = True):
super().__init__()
ParameterValidator.assert_type_and_value(percentage,
float,
"TCRdistClassifier",
"percentage",
min_inclusive=0.,
max_inclusive=1.)
self.percentage = percentage
self.k = None
self.label = None
self.show_warnings = show_warnings
def _parse_settings(self, instruction: dict,
symbol_table: SymbolTable) -> list:
try:
settings = []
for index, setting in enumerate(instruction["settings"]):
if "preprocessing" in setting:
ParameterValidator.assert_type_and_value(
setting["preprocessing"], str,
TrainMLModelParser.__name__, f'settings: {index+1}. '
f'element: preprocessing')
if symbol_table.contains(setting["preprocessing"]):
preprocessing_sequence = symbol_table.get(
setting["preprocessing"])
preproc_name = setting["preprocessing"]
else:
raise KeyError(
f"{TrainMLModelParser.__name__}: preprocessing was set in the TrainMLModel instruction to value "
f"{setting['preprocessing']}, but no such preprocessing was defined in the specification under "
f"definitions: {PreprocessingParser.keyword}.")
else:
setting["preprocessing"] = None
preprocessing_sequence = []
preproc_name = None
ParameterValidator.assert_keys(
setting.keys(), ["preprocessing", "ml_method", "encoding"],
TrainMLModelParser.__name__,
f"settings, {index + 1}. entry")
encoder = symbol_table.get(setting["encoding"]).build_object(symbol_table.get(instruction["dataset"]),
**symbol_table.get_config(setting["encoding"])["encoder_params"])\
.set_context({"dataset": symbol_table.get(instruction['dataset'])})
s = HPSetting(encoder=encoder,
encoder_name=setting["encoding"],
encoder_params=symbol_table.get_config(
setting["encoding"])["encoder_params"],
ml_method=symbol_table.get(setting["ml_method"]),
ml_method_name=setting["ml_method"],
ml_params=symbol_table.get_config(
setting["ml_method"]),
preproc_sequence=preprocessing_sequence,
preproc_sequence_name=preproc_name)
settings.append(s)
return settings
except KeyError as key_error:
raise KeyError(
f"{TrainMLModelParser.__name__}: parameter {key_error.args[0]} was not defined under settings in TrainMLModel instruction."
)
def check_export_format(specs: dict, tool_name: str,
instruction_name: str):
ParameterValidator.assert_keys_present(
list(specs['instructions'][instruction_name].keys()),
["export_formats"], tool_name,
f"{instruction_name}/export_formats")
ParameterValidator.assert_type_and_value(
specs['instructions'][instruction_name]["export_formats"], list,
tool_name, f"{instruction_name}/export_formats")
assert len(specs['instructions'][instruction_name]["export_formats"]) == 1, \
f"{tool_name}: only one format can be specified under export_formats parameter under " \
f"{instruction_name}/export_formats, got {specs['instructions'][instruction_name]['export_formats']} instead."
return specs['instructions'][instruction_name]["export_formats"][0]
def parse_signals(signals: dict, symbol_table: SymbolTable):
for key, signal_spec in signals.items():
ParameterValidator.assert_keys_present(signal_spec.keys(), SignalParser.VALID_KEYS, "SignalParser", key)
implanting_strategy = SignalParser._get_implanting_strategy(key, signal_spec)
ParameterValidator.assert_keys(signal_spec["motifs"], symbol_table.get_keys_by_type(SymbolType.MOTIF), "SignalParser",
f"motifs in signal {key}", False)
signal_motifs = [symbol_table.get(motif_id) for motif_id in signal_spec["motifs"]]
signal = Signal(key, signal_motifs, implanting_strategy)
symbol_table.add(key, SymbolType.SIGNAL, signal)
return symbol_table, signals
def build_object(cls, **kwargs):
if kwargs["additional_node_attributes"] is None:
kwargs["additional_node_attributes"] = []
if kwargs["additional_edge_attributes"] is None:
kwargs["additional_edge_attributes"] = []
ParameterValidator.assert_type_and_value(
kwargs["additional_node_attributes"], list,
"CytoscapeNetworkExporter", "additional_node_attributes")
ParameterValidator.assert_type_and_value(
kwargs["additional_edge_attributes"], list,
"CytoscapeNetworkExporter", "additional_edge_attributes")
return CytoscapeNetworkExporter(**kwargs)
def __init__(self, k: int, skip_first_n_aa: int, skip_last_n_aa: int, abundance: str, normalize_all_features: bool, name: str = None):
location = "AtchleyKmerEncoder"
ParameterValidator.assert_type_and_value(k, int, location, "k", 1)
ParameterValidator.assert_type_and_value(skip_first_n_aa, int, location, "skip_first_n_aa", 0)
ParameterValidator.assert_type_and_value(skip_last_n_aa, int, location, "skip_last_n_aa", 0)
ParameterValidator.assert_in_valid_list(abundance.upper(), [ab.name for ab in RelativeAbundanceType], location, "abundance")
ParameterValidator.assert_type_and_value(normalize_all_features, bool, location, "normalize_all_features")
self.k = k
self.skip_first_n_aa = skip_first_n_aa
self.skip_last_n_aa = skip_last_n_aa
self.abundance = RelativeAbundanceType[abundance.upper()]
self.normalize_all_features = normalize_all_features
self.name = name
self.scaler_path = None
self.vectorizer_path = None
def _prepare_parameters(distance_metric: str,
attributes_to_match: list,
sequence_batch_size: int,
context: dict = None):
valid_metrics = [metric.name for metric in DistanceMetricType]
ParameterValidator.assert_in_valid_list(distance_metric, valid_metrics,
"DistanceEncoder",
"distance_metric")
return {
"distance_metric": DistanceMetricType[distance_metric.upper()],
"attributes_to_match": attributes_to_match,
"sequence_batch_size": sequence_batch_size,
"context": context
}
def build_object(cls, **kwargs):
location = "DeepRCMotifDiscovery"
name = kwargs["name"] if "name" in kwargs else None
ParameterValidator.assert_type_and_value(kwargs["n_steps"],
int,
location,
"n_steps",
min_inclusive=1)
ParameterValidator.assert_type_and_value(kwargs["threshold"],
float,
location,
"threshold",
min_inclusive=0,
max_inclusive=1)
return DeepRCMotifDiscovery(n_steps=kwargs["n_steps"],
threshold=kwargs["threshold"],
name=name)
def parse_motifs(motifs: dict, symbol_table: SymbolTable):
valid_motif_keys = [
"seed", "instantiation", "seed_chain1", "seed_chain2",
"name_chain1", "name_chain2"
]
for key in motifs.keys():
ParameterValidator.assert_keys(motifs[key].keys(),
valid_motif_keys,
"MotifParser",
key,
exclusive=False)
motif = MotifParser._parse_motif(key, motifs[key])
symbol_table.add(key, SymbolType.MOTIF, motif)
return symbol_table, motifs
def parse_encoder(key: str, specs: dict):
class_path = "encodings"
valid_encoders = ReflectionHandler.all_nonabstract_subclass_basic_names(
DatasetEncoder, "Encoder", class_path)
encoder = ObjectParser.get_class(specs, valid_encoders, "Encoder",
class_path, "EncodingParser", key)
params = ObjectParser.get_all_params(specs, class_path,
encoder.__name__[:-7], key)
required_params = [
p for p in list(
inspect.signature(encoder.__init__).parameters.keys())
if p != "self"
]
ParameterValidator.assert_all_in_valid_list(
params.keys(), required_params, "EncoderParser",
f"{key}/{encoder.__name__.replace('Encoder', '')}")
return encoder, params
def parse(self,
key: str,
instruction: dict,
symbol_table: SymbolTable,
path: str = None) -> ExploratoryAnalysisInstruction:
exp_analysis_units = {}
ParameterValidator.assert_keys(instruction, ["analyses", "type"],
"ExploratoryAnalysisParser",
"ExploratoryAnalysis")
for analysis_key, analysis in instruction["analyses"].items():
params = self._prepare_params(analysis, symbol_table)
exp_analysis_units[analysis_key] = ExploratoryAnalysisUnit(
**params)
process = ExploratoryAnalysisInstruction(
exploratory_analysis_units=exp_analysis_units, name=key)
return process
def parse_instruction(key: str, instruction: dict,
symbol_table: SymbolTable, path) -> tuple:
ParameterValidator.assert_keys_present(list(instruction.keys()),
["type"],
InstructionParser.__name__, key)
valid_instructions = [
cls[:-6]
for cls in ReflectionHandler.discover_classes_by_partial_name(
"Parser", "dsl/instruction_parsers/")
]
ParameterValidator.assert_in_valid_list(instruction["type"],
valid_instructions,
"InstructionParser", "type")
parser = ReflectionHandler.get_class_by_name(
"{}Parser".format(instruction["type"]), "instruction_parsers/")()
instruction_object = parser.parse(key, instruction, symbol_table, path)
symbol_table.add(key, SymbolType.INSTRUCTION, instruction_object)
return instruction, symbol_table
