Given dimension and population metrics, computes a timeseries of drift metrics that measures the dimensional drift of the current data compared to a lookback baseline. This operator requires a single input timeseries and derives the baseline dataset for each timestamp according to the lookback and seasonality parameters. For example, the baseline dataset for timestamp 2023-01-29 with a 28 day lookback period and 7 day seasonality will contain the all the data points at the timestamps 2023-01-01, 2023-01-08, 2023-01-15, and 2023-01-22. The dimension values can be encoded in two ways: enumeration or linear buckets.
  • The enumeration encoder should be used for string values or low-cardinality numeric values. New values encountered in the current data are always mapped the same integer, which corresponds to the other category.
  • The linear buckets encoder should be used for numerical values that have high cardinality. The linear buckets encoder maps each dimension value to a bucket index. All values less than the smallest bucket are mapped to the smallest bucket. All values larger than the largest bucket are mapped to the largest bucket.
At each timestamp, the operator computes a normalized EMD of the distribution of dimension values of the current data and compared to the baseline data. The EMD distance function depends on the encoding:
  • For enumeration, the distance used is always 1 / number of unique values.
  • For bucketing, the distance used is the normalized distance between the buckets.

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Inputs

targetPropertydescription
currentDataA data frame with columns ts, dim, and met. Missing data is ok, and the time index filler is not recommended. Column ts is the timestamp. Column dim is the dimension to used to measure drift. Column met is the metric or population of the dimension value at the timestamp.

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Outputs

outputNamedescription
driftScoreDataThe resulting data frame of drift metrics. The data frame has columns ts and met. Column ts is the timestamp. Column met is the drift score computed at the timestamp.

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Parameters

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Operator parameters

namedescription
monitoringGranularityThe time granularity of the output timeseries.
seasonalityPeriodThe step-size used when building the baseline dataset
lookbackThe period of time to consider points for the baseline dataset.
encoderThe encoder parameters json object.

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Encoder parameters

namedescription
typeThe type of encoder. One of ENUMERATION or LINEAR_BUCKETS.
linearBucketThe linear bucket parameters json object.

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Linear bucket parameters

namedescription
bucketStartThe value corresponding to the first bucket.
bucketSizeThe size of each bucket.
bucketCountThe number of buckets to use.

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Example

{
  "name": "driftScore",
  "type": "LookbackBaselineDrift",
  "params": {
    "lookback": "P28D",
    "monitoringGranularity": "P1D",
    "seasonalityPeriod": "P7D",
    "encoder": {
      "type": "LINEAR_BUCKETS",
      "linearBucket": {
        "bucketStart": "0",
        "bucketSize": "1",
        "bucketCount": "100"
      }
    }
  },
  "inputs": [
    {
      "targetProperty": "currentData",
      "sourcePlanNode": "currentDataFetcher",
      "sourceProperty": "currentData"
    }
  ],
  "outputs": [
    {
      "outputName": "driftScoreData"
    }
  ]
}