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197bf2c563
Open sourcing Aggregation Framework, a config-driven Summingbird based framework for generating real-time and batch aggregate features to be consumed by ML models.
35 lines
1.5 KiB
Scala
35 lines
1.5 KiB
Scala
package com.twitter.timelines.data_processing.ml_util.aggregation_framework.metrics
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import com.twitter.ml.api._
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/**
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* A "human-readable" metric that can be applied to features of multiple
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* different types. Wrapper around AggregationMetric used as syntactic sugar
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* for easier config.
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*/
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trait EasyMetric extends Serializable {
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/*
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* Given a feature type, fetches the corrrect underlying AggregationMetric
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* to perform this operation over the given feature type, if any. If no such
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* metric is available, returns None. For example, MEAN cannot be applied
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* to FeatureType.String and would return None.
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*
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* @param featureType Type of feature to fetch metric for
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* @param useFixedDecay Param to control whether the metric should use fixed decay
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* logic (if appropriate)
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* @return Strongly typed aggregation metric to use for this feature type
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*
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* For example, if the EasyMetric is MEAN and the featureType is
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* FeatureType.Continuous, the underlying AggregationMetric should be a
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* scalar mean. If the EasyMetric is MEAN and the featureType is
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* FeatureType.SparseContinuous, the AggregationMetric returned could be a
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* "vector" mean that averages sparse maps. Using the single logical name
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* MEAN for both is nice syntactic sugar making for an easier to read top
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* level config, though different underlying operators are used underneath
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* for the actual implementation.
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*/
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def forFeatureType[T](
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featureType: FeatureType,
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): Option[AggregationMetric[T, _]]
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}
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