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Showing content from http://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/feature/MinMaxScaler.html below:

MinMaxScaler (Spark 4.0.0 JavaDoc)

All Implemented Interfaces:

Serializable, org.apache.spark.internal.Logging, MinMaxScalerParams, Params, HasInputCol, HasOutputCol, DefaultParamsWritable, Identifiable, MLWritable

Rescale each feature individually to a common range [min, max] linearly using column summary statistics, which is also known as min-max normalization or Rescaling. The rescaled value for feature E is calculated as:

$$ Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min $$

For the case \(E_{max} == E_{min}\), \(Rescaled(e_i) = 0.5 * (max + min)\).

See Also:

• Serialized Form

Note:

Since zero values will probably be transformed to non-zero values, output of the transformer will be DenseVector even for sparse input.

• Nested Class Summary
  Nested classes/interfaces inherited from interface org.apache.spark.internal.Loggingorg.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

• Constructor Summary

  Constructors

• Method Summary

  Creates a copy of this instance with the same UID and some extra params.

  Fits a model to the input data.

  Param for input column name.

  upper bound after transformation, shared by all features Default: 1.0

  lower bound after transformation, shared by all features Default: 0.0

  Param for output column name.

  Check transform validity and derive the output schema from the input schema.

  An immutable unique ID for the object and its derivatives.

  Methods inherited from interface org.apache.spark.internal.LogginginitializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
  Methods inherited from interface org.apache.spark.ml.util.MLWritablesave Methods inherited from interface org.apache.spark.ml.param.Paramsclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

• Constructor Details
  • MinMaxScaler public MinMaxScaler(String uid)
  • MinMaxScaler

    public MinMaxScaler()

• Method Details
  • load
  • read
  • min

    lower bound after transformation, shared by all features Default: 0.0

    Specified by:

    min in interface MinMaxScalerParams

    Returns:

    (undocumented)

  • max

    upper bound after transformation, shared by all features Default: 1.0

    Specified by:

    max in interface MinMaxScalerParams

    Returns:

    (undocumented)

  • outputCol

    Param for output column name.

    Specified by:

    outputCol in interface HasOutputCol

    Returns:

    (undocumented)

  • inputCol

    Param for input column name.

    Specified by:

    inputCol in interface HasInputCol

    Returns:

    (undocumented)

  • uid

    An immutable unique ID for the object and its derivatives.

    Specified by:

    uid in interface Identifiable

    Returns:

    (undocumented)

  • setInputCol
  • setOutputCol
  • setMin
  • setMax
  • fit

    Fits a model to the input data.

    Specified by:

    fit in class Estimator<MinMaxScalerModel>

    Parameters:

    dataset - (undocumented)

    Returns:

    (undocumented)

  • transformSchema

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Specified by:

    transformSchema in class PipelineStage

    Parameters:

    schema - (undocumented)

    Returns:

    (undocumented)

  • copy Description copied from interface: Params

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Specified by:

    copy in interface Params

    Specified by:

    copy in class Estimator<MinMaxScalerModel>

    Parameters:

    extra - (undocumented)

    Returns:

    (undocumented)

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