def test_parse_reports(self):
reports = {"r1": {"SequenceLengthDistribution": {}}}
symbol_table = SymbolTable()
symbol_table, specs = ReportParser.parse_reports(reports, symbol_table)
self.assertTrue(symbol_table.contains("r1"))
self.assertTrue(
isinstance(symbol_table.get("r1"), SequenceLengthDistribution))
def _parse_settings(self, instruction: dict,
symbol_table: SymbolTable) -> list:
try:
settings = []
for index, setting in enumerate(instruction["settings"]):
if "preprocessing" in setting and setting[
"preprocessing"] is not None:
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"]
if not all(preproc.keeps_example_count()
for preproc in preprocessing_sequence):
raise ValueError(
f"{TrainMLModelParser.__name__}: preprocessing sequence {preproc_name} includes preprocessing that "
f"change the number of examples at runtime and as such cannot be used with this instruction. See the "
f"documentation for the preprocessing or alternatively use them with other instructions."
)
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'])})
ml_method = symbol_table.get(setting["ml_method"])
ml_method.check_encoder_compatibility(encoder)
s = HPSetting(encoder=encoder,
encoder_name=setting["encoding"],
encoder_params=symbol_table.get_config(
setting["encoding"])["encoder_params"],
ml_method=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 _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 test_parse_simulation(self):
simulation = {
"sim1": {
"var1": {
"signals": ["signal1"],
"dataset_implanting_rate": 0.5,
"repertoire_implanting_rate": 0.1
}
}
}
symbol_table = SymbolTable()
symbol_table.add("motif1", SymbolType.MOTIF, Motif("motif1", GappedKmerInstantiation(position_weights={0: 1}), seed="CAS"))
symbol_table.add("signal1", SymbolType.SIGNAL, Signal("signal1", [symbol_table.get("motif1")],
HealthySequenceImplanting(GappedMotifImplanting(), implanting_computation=ImplantingComputation.ROUND)))
symbol_table, specs = SimulationParser.parse_simulations(simulation, symbol_table)
self.assertTrue(symbol_table.contains("sim1"))
sim1 = symbol_table.get("sim1")
self.assertEqual(1, len(sim1.implantings))