ComputePerInstanceStatistics

class ComputePerInstanceStatistics.ComputePerInstanceStatistics(evaluationMetric='all', labelCol=None, scoredLabelsCol=None, scoredProbabilitiesCol=None, scoresCol=None)[source]

Bases: mmlspark.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaTransformer

Evaluates the given scored dataset with per instance metrics.

The Regression metrics are:

  • “L1_loss”
  • “L2_loss”

The Classification metrics are:

  • “log_loss”
Parameters:
  • evaluationMetric (str) – Metric to evaluate models with (default: all)
  • labelCol (str) – The name of the label column
  • scoredLabelsCol (str) – Scored labels column name, only required if using SparkML estimators
  • scoredProbabilitiesCol (str) – Scored probabilities, usually calibrated from raw scores, only required if using SparkML estimators
  • scoresCol (str) – Scores or raw prediction column name, only required if using SparkML estimators
getEvaluationMetric()[source]
Returns:Metric to evaluate models with (default: all)
Return type:str
static getJavaPackage()[source]

Returns package name String.

getLabelCol()[source]
Returns:The name of the label column
Return type:str
getScoredLabelsCol()[source]
Returns:Scored labels column name, only required if using SparkML estimators
Return type:str
getScoredProbabilitiesCol()[source]
Returns:Scored probabilities, usually calibrated from raw scores, only required if using SparkML estimators
Return type:str
getScoresCol()[source]
Returns:Scores or raw prediction column name, only required if using SparkML estimators
Return type:str
classmethod read()[source]

Returns an MLReader instance for this class.

setEvaluationMetric(value)[source]
Parameters:evaluationMetric (str) – Metric to evaluate models with (default: all)
setLabelCol(value)[source]
Parameters:labelCol (str) – The name of the label column
setParams(evaluationMetric='all', labelCol=None, scoredLabelsCol=None, scoredProbabilitiesCol=None, scoresCol=None)[source]

Set the (keyword only) parameters

Parameters:
  • evaluationMetric (str) – Metric to evaluate models with (default: all)
  • labelCol (str) – The name of the label column
  • scoredLabelsCol (str) – Scored labels column name, only required if using SparkML estimators
  • scoredProbabilitiesCol (str) – Scored probabilities, usually calibrated from raw scores, only required if using SparkML estimators
  • scoresCol (str) – Scores or raw prediction column name, only required if using SparkML estimators
setScoredLabelsCol(value)[source]
Parameters:scoredLabelsCol (str) – Scored labels column name, only required if using SparkML estimators
setScoredProbabilitiesCol(value)[source]
Parameters:scoredProbabilitiesCol (str) – Scored probabilities, usually calibrated from raw scores, only required if using SparkML estimators
setScoresCol(value)[source]
Parameters:scoresCol (str) – Scores or raw prediction column name, only required if using SparkML estimators