示例#1
0
class TaskWithCustomValue(PythonTask):
    custom_value = parameter.type(_MyCustomObjectValueType)
    list_of_customs = parameter.sub_type(_MyCustomObjectValueType)[List]

    report = output[str]

    def run(self):
        assert isinstance(self.custom_value, MyCustomObject)
        assert isinstance(self.list_of_customs[1], MyCustomObject)
        self.report = self.custom_value.custom + self.list_of_customs[1].custom
示例#2
0
    DateValueType,
    ObjectValueType,
    TargetPathValueType,
    TargetValueType,
    TimeDeltaValueType,
)
from targets.values.custom_datetime_values import (
    DateHourValueType,
    DateMinuteValueType,
    DateSecondValueType,
    MonthValueType,
    YearValueType,
)
from targets.values.timedelta_value import DateIntervalValueType

Parameter = parameter.type(ObjectValueType)

StrParameter = parameter[str]
IntParameter = parameter[int]
FloatParameter = parameter[float]
BoolParameter = parameter[bool]

DictParameter = parameter[Dict]
ListParameter = parameter[List]
SetParameter = parameter[Set]
FileListParameter = parameter[List[str]]
ListStrParameter = parameter[List[str]]
TupleParameter = parameter[Tuple]

# target parameters
TargetParameter = parameter.type(TargetValueType)
    type = FeatureStore

    def load_from_target(self, target, **kwargs):
        features = target.partition("features").load(DataFrame)
        targets = target.partition("targets").load(DataFrame)
        return FeatureStore(features=features, targets=targets)

    def save_to_target(self, target, value, **kwargs):
        target.partition("features").save(value.features)
        target.partition("targets").save(value.targets)
        target.mark_success()


FeatureStoreParameter = register_custom_parameter(
    value_type=MyFeatureStoreValueType,
    parameter=parameter.type(MyFeatureStoreValueType).folder.hdf5,
)

###############
# USAGE


@task(result=FeatureStoreParameter.output)
def create_feature_store(ratio=1):
    features = pd.DataFrame(data=[[ratio, 2], [2, 3]],
                            columns=["Names", "Births"])
    targets = pd.DataFrame(data=[[1, 22], [2, 33]], columns=["Names", "Class"])
    return FeatureStore(features=features, targets=targets)


@task
示例#4
0
from dbnd import PipelineTask, PythonTask, output, parameter
from targets.dir_target import DirTarget
from targets.target_config import file
from targets.values import TargetValueType

logger = logging.getLogger(__name__)


class MyCustomFolder(DirTarget):
    def __init__(self, *args, **kwargs):
        super(MyCustomFolder, self).__init__(*args, **kwargs)
        self.scores = self.partition("scores", config=file.csv)
        self.reports = self.partition("reports", config=file.csv)


my_custom_target = parameter.type(TargetValueType).custom_target(
    MyCustomFolder, folder=True)


class TaskWithCustomOutput(PythonTask):
    custom = my_custom_target.output

    def run(self):
        self.custom.scores.write("1")
        self.custom.reports.write("2")
        self.custom.mark_success()


class TaskWithCustomInput(PythonTask):
    custom = my_custom_target
    t_output = output.data