StatFunctions

Type Members

class QuantileSummaries extends Serializable

Helper class to compute approximate quantile summary.
Helper class to compute approximate quantile summary. This implementation is based on the algorithm proposed in the paper: "Space-efficient Online Computation of Quantile Summaries" by Greenwald, Michael and Khanna, Sanjeev. (http://dx.doi.org/10.1145/375663.375670)
In order to optimize for speed, it maintains an internal buffer of the last seen samples, and only inserts them after crossing a certain size threshold. This guarantees a near-constant runtime complexity compared to the original algorithm.

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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object QuantileSummaries extends Serializable
final def asInstanceOf[T0]: T0

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def calculateCov(df: DataFrame, cols: Seq[String]): Double

Calculate the covariance of two numerical columns of a DataFrame.
Calculate the covariance of two numerical columns of a DataFrame.
df
The DataFrame
cols
the column names
returns
the covariance of the two columns.
def clone(): AnyRef

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protected[java.lang]
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def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame

Generate a table of frequencies for the elements of two columns.
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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final def getClass(): Class[_]

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def hashCode(): Int

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def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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final def isInstanceOf[T0]: Boolean

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def isTraceEnabled(): Boolean

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def log: Logger

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def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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def logDebug(msg: ⇒ String): Unit

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def logError(msg: ⇒ String, throwable: Throwable): Unit

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def logError(msg: ⇒ String): Unit

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def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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def logInfo(msg: ⇒ String): Unit

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def logName: String

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def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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def logTrace(msg: ⇒ String): Unit

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def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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def logWarning(msg: ⇒ String): Unit

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def multipleApproxQuantiles(df: DataFrame, cols: Seq[String], probabilities: Seq[Double], relativeError: Double): Seq[Seq[Double]]

Calculates the approximate quantiles of multiple numerical columns of a DataFrame in one pass.
Calculates the approximate quantiles of multiple numerical columns of a DataFrame in one pass.
The result of this algorithm has the following deterministic bound: If the DataFrame has N elements and if we request the quantile at probability p up to error err, then the algorithm will return a sample x from the DataFrame so that the *exact* rank of x is close to (p * N). More precisely,
floor((p - err) * N) <= rank(x) <= ceil((p + err) * N).
This method implements a variation of the Greenwald-Khanna algorithm (with some speed optimizations). The algorithm was first present in Space-efficient Online Computation of Quantile Summaries by Greenwald and Khanna.
df
the dataframe
cols
numerical columns of the dataframe
probabilities
a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the minimum, 0.5 is the median, 1 is the maximum.
relativeError
The relative target precision to achieve (>= 0). If set to zero, the exact quantiles are computed, which could be very expensive. Note that values greater than 1 are accepted but give the same result as 1.
returns
for each column, returns the requested approximations
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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def pearsonCorrelation(df: DataFrame, cols: Seq[String]): Double

Calculate the Pearson Correlation Coefficient for the given columns
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

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final def wait(): Unit

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final def wait(arg0: Long, arg1: Int): Unit

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final def wait(arg0: Long): Unit

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Related Doc: package stat

object StatFunctions extends Logging

Type Members

class QuantileSummaries extends Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

object QuantileSummaries extends Serializable

final def asInstanceOf[T0]: T0

def calculateCov(df: DataFrame, cols: Seq[String]): Double

def clone(): AnyRef

def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

def multipleApproxQuantiles(df: DataFrame, cols: Seq[String], probabilities: Seq[Double], relativeError: Double): Seq[Seq[Double]]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def pearsonCorrelation(df: DataFrame, cols: Seq[String]): Double

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Logging

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