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 _prepare_optional_params(self, analysis: dict,
symbol_table: SymbolTable,
yaml_location: str) -> dict:
params = {}
dataset = symbol_table.get(analysis["dataset"])
if "encoding" in analysis:
params["encoder"] = symbol_table.get(
analysis["encoding"]).build_object(
dataset,
**symbol_table.get_config(
analysis["encoding"])["encoder_params"])
if "labels" in analysis:
params["label_config"] = LabelHelper.create_label_config(
analysis["labels"], dataset,
ExploratoryAnalysisParser.__name__, yaml_location)
else:
params["label_config"] = LabelConfiguration()
if "preprocessing_sequence" in analysis:
params["preprocessing_sequence"] = symbol_table.get(
analysis["preprocessing_sequence"])
return params
def _prepare_optional_params(self, analysis: dict, symbol_table: SymbolTable) -> dict:
params = {}
dataset = symbol_table.get(analysis["dataset"])
if "encoding" in analysis:
params["encoder"] = symbol_table.get(analysis["encoding"]).build_object(dataset, **symbol_table.get_config(analysis["encoding"])["encoder_params"])
params["label_config"] = LabelConfiguration()
if "labels" in analysis:
for label in analysis["labels"]:
label_values = self._get_label_values(label, dataset)
params["label_config"].add_label(label, label_values)
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