Configure metrics with YAML about insight HOT 6 CLOSED

class MetricFactory():

    def metric_from_dict(self, d: Dict[str, Any]) -> Metric:
        ...

"""
Option 1: MetricFactory in base.py
    Step 1: Create a registry of all the metrics.
        Substep: recurse through _Metric children in depth first search and add to to _registry with 'name' as the key
            and class reference as the value.
    Step 2: given a dictionary which contains the name of the metric and parameters, call the metric from _registry.
"""
class MetricFactory:
    _registry: Dict[str, Any] = {}

    @classmethod
    def _update_registry_recuresively(cls, next_metric):
        if next_metric.name is not None:
            cls._registry.append({next_metric.name: next_metric})
        for subclass in next_metric.__subclasses__():
            cls._update_registry_recuresively(subclass)


    @classmethod
    def metric_from_dict(cls, bluprnt: Dict[str, any]):
        if len (cls._registry) == 0:
            cls._update_registry_recuresively(_Metric)

        # STEP: Create a list of named parameters based on dictionary entries to pass into the Metric constructor.

        new_metric = cls._registry[bluprnt['name']](...)
        return new_metric



# Benefit: simple and does not interfere with the metrics.

"""
Option 2: MetricFactory in metric.py or in a separate module.
    Step 1: Create a registry of all the metrics.
        Substep: recurse through OneColumnMetric, TwoColumnMetric, DataFrameMetric, and TwoDataFrameMetric children in 
        depth first search and add to to _registry with 'name' as the key and class reference as the value.
    Step 2: given a dictionary which contains the name of the metric and parameters, call the metric from _registry.
"""


class MetricFactory:
    _registry: Dict[str, Any] = {}

    @classmethod
    def _update_registry_recuresively(cls, next_metric):
        if next_metric.name is not None:
            cls._registry.append({next_metric.name: next_metric})
        for subclass in next_metric.__subclasses__():
            cls._update_registry_recuresively(subclass)

    @classmethod
    def metric_from_dict(cls, bluprnt: Dict[str, any]):
        if len(cls._registry) == 0:
            cls._update_registry_recuresively(OneColumnMetric)
            cls._update_registry_recuresively(TwoColumnMetric)
            cls._update_registry_recuresively(DataFrameMetric)
            cls._update_registry_recuresively(TwoDataFrameMetric)

        # STEP: Create a list of named parameters based on dictionary entries to pass into the Metric constructor.

        new_metric = cls._registry[bluprnt['name']](...)
        return new_metric


# Benefit: does not interfere with the metrics. Does not interfere with a private class.

"""
Option 3: As a class method of _Metric.
    Step 1: Create a registry of all the metrics.
        Substep: recurse through OneColumnMetric, TwoColumnMetric, DataFrameMetric, and TwoDataFrameMetric children in 
        depth first search and add to to _registry with 'name' as the key and class reference as the value.
    Step 2: given a dictionary which contains the name of the metric and parameters, call the metric from _registry.
"""


class _Metric:
    _registry: Dict[str, Any] = {}

    @classmethod
    def _update_registry_recuresively(cls, next_metric):
        if next_metric.name is not None:
            cls._registry.append({next_metric.name: next_metric})
        for subclass in next_metric.__subclasses__():
            cls._update_registry_recuresively(subclass)

    @classmethod
    def metric_from_dict(cls, bluprnt: Dict[str, any]):
        if len(cls._registry) == 0:
            cls._update_registry_recuresively(cls)

        # STEP: Create a list of named parameters based on dictionary entries to pass into the Metric constructor.

        new_metric = cls._registry[bluprnt['name']](...)
        return new_metric


# Benefit: Can call from any metric with the same result.