mlm_insights.core.metrics.interfaces package¶
Submodules¶
mlm_insights.core.metrics.interfaces.dataset_metric_base module¶
- class mlm_insights.core.metrics.interfaces.dataset_metric_base.DatasetMetricBase(config: ~typing.Dict[str, ~mlm_insights.constants.definitions.ConfigParameter] = <factory>)¶
Bases:
ABCAbstract Base Class for defining an Insights Dataset Metric. Dataset Metric is a metric applicable at the Dataset level. Metric-subclasses must override get_result method to produce the computed value of a Metric
- compute(dataset: DataFrame, **kwargs: Any) None¶
Calculate the metric value(s) from the passed DataFrame , set the internal state with the value(s). When a metric is being computed for a partitioned data set, this method is invoked for each partition. Write logic required to derive the metric value in this method for that specific partition
Parameters¶
dataset : pd.DataFrame DataFrame object for either the entire dataset or a partition on which a Metric is being computed
- config: Dict[str, ConfigParameter]¶
- abstract classmethod create(config: Dict[str, ConfigParameter] | None = None, **kwargs: Any) DatasetMetricBase¶
Factory Method to create an object. The configuration will be available in config.
Returns¶
- MetricBase
An Instance of MetricBase.
- classmethod do_deserialize(metric_message: MetricMessage) DatasetMetricBase¶
- do_serialize() MetricMessage¶
Method to do metric serialization. It handles both OOB and custom serialization of metric
Returns¶
MetricMessage protobuf message
- get_config() Dict[str, ConfigParameter]¶
- classmethod get_name() str¶
Returns the Shareable Dataset Components that a Metric requires to compute its state and values Metrics which do not require SDC need not override this property
Returns¶
List of SDCMetaData. Each SDCMetaData must contain the klass attribute which points to the SDC class
Returns the Shareable Feature Components that a Metric requires to compute its state and values Metrics which do not require SFC need not override this property
Returns¶
Dict where feature_name as key and List of SFCMetadata as value. Each SFCMetadata must contain the klass attribute which points to the SFC class
- abstract get_result(**kwargs: Any) Dict[str, Any]¶
Returns the computed value of the metric
Returns¶
Dict[str, Any]: Dictionary with key as string and value as any metric property.
- get_standard_metric_result(**kwargs: Any) StandardMetricResult¶
This method returns metric output in standard format.
Returns¶
StandardMetricResult
- merge(other_metric: DatasetMetricBase, **kwargs: Any) DatasetMetricBase¶
Merge the other metric with the current metric and return a new instance of metric. Use this method to merge the states of the 2 metrics to produce a statistically-correctly state Note: you should not mutate the current metric but create a new instance.
Parameters¶
other_metric : DatasetMetricBase The second metric which the current metric is being merged with
Returns¶
DatasetMetricBase: New, merged DatasetMetricBase instance
- set_config(config: Dict[str, ConfigParameter]) DatasetMetricBase¶
mlm_insights.core.metrics.interfaces.metric_base module¶
- class mlm_insights.core.metrics.interfaces.metric_base.MetricBase(config: ~typing.Dict[str, ~mlm_insights.constants.definitions.ConfigParameter] = <factory>)¶
Bases:
ABCAbstract Base Class for defining an Insights Metric. Metric-subclasses must override get_result method to produce the computed value of a Metric
- compute(column: Series, **kwargs: Any) None¶
Calculate the metric value(s) from the passed series object, set the internal state with the value(s). When a metric is being computed for a partitioned data set, this method is invoked for each partition. Write logic required to derive the metric value in this method for that specific partition Shareable Feature Component(s) can be accessed using kwargs
Parameters¶
column : pd.Series Series object for a Feature on which a Metric is being computed
- config: Dict[str, ConfigParameter]¶
- abstract classmethod create(config: Dict[str, ConfigParameter] | None = None) MetricBase¶
Factory Method to create an object. The configuration will be available in config.
Returns¶
- MetricBase
An Instance of MetricBase.
- classmethod do_deserialize(metric_message: MetricMessage) MetricBase¶
- do_serialize() MetricMessage¶
Method to do metric serialization. It handles both OOB and custom serialization of metric
Returns¶
MetricMessage protobuf message
- get_config() Dict[str, ConfigParameter]¶
- classmethod get_name() str¶
Returns the Shareable Feature Components that a Metric requires to compute its state and values Metrics which do not require SFC need not override this property
Returns¶
List of SFCMetadata. Each SFCMetadata must contain the klass attribute which points to the SFC class
- abstract get_result(**kwargs: Any) Dict[str, Any]¶
Returns the computed value of the metric Shareable Feature Component(s) can be accessed using kwargs
Returns¶
Dict[str, Any]: Dictionary with key as string and value as any metric property.
- get_standard_metric_result(**kwargs: Any) StandardMetricResult¶
This method returns metric output in standard format.
Returns¶
StandardMetricResult
- classmethod get_supported_variable_types() List[VariableType]¶
Method to retrieve the list of Feature Variable type supported for the metric
Returns¶
List of Feature Variable type supported by the metric
- merge(other_metric: MetricBase, **kwargs: Any) MetricBase¶
Merge the other metric with the current metric and return a new instance of metric. Use this method to merge the states of the 2 metrics to produce a statistically-correctly state Note: you should not mutate the current metric but create a new instance. Shareable Feature Component(s) can be accessed using kwargs
Parameters¶
other_metric : MetricBase The second metric which the current metric is being merged with
Returns¶
MetricBase: New, merged Metric instance
- set_config(config: Dict[str, ConfigParameter]) MetricBase¶