com.google.api.services.bigquery.model

Class RankingMetrics

java.lang.Object

java.util.AbstractMap<String,Object>

com.google.api.client.util.GenericData

com.google.api.client.json.GenericJson

com.google.api.services.bigquery.model.RankingMetrics

All Implemented Interfaces:

Cloneable, Map<String,Object>

public final class RankingMetrics
extends com.google.api.client.json.GenericJson

Evaluation metrics used by weighted-ALS models specified by feedback_type=implicit.

This is the Java data model class that specifies how to parse/serialize into the JSON that is transmitted over HTTP when working with the BigQuery API. For a detailed explanation see: https://developers.google.com/api-client-library/java/google-http-java-client/json

Author:

Google, Inc.

Nested Class Summary

Nested classes/interfaces inherited from class com.google.api.client.util.GenericData

com.google.api.client.util.GenericData.Flags

Nested classes/interfaces inherited from class java.util.AbstractMap

AbstractMap.SimpleEntry<K,V>, AbstractMap.SimpleImmutableEntry<K,V>

Nested classes/interfaces inherited from interface java.util.Map

Map.Entry<K,V>

Constructor Summary

Constructors

Constructor and Description
RankingMetrics()

Method Summary

Modifier and Type	Method and Description
RankingMetrics	clone()
Double	getAverageRank() Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.
Double	getMeanAveragePrecision() Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.
Double	getMeanSquaredError() Similar to the mean squared error computed in regression and explicit recommendation models except instead of computing the rating directly, the output from evaluate is computed against a preference which is 1 or 0 depending on if the rating exists or not.
Double	getNormalizedDiscountedCumulativeGain() A metric to determine the goodness of a ranking calculated from the predicted confidence by comparing it to an ideal rank measured by the original ratings.
RankingMetrics	set(String fieldName, Object value)
RankingMetrics	setAverageRank(Double averageRank) Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.
RankingMetrics	setMeanAveragePrecision(Double meanAveragePrecision) Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.
RankingMetrics	setMeanSquaredError(Double meanSquaredError) Similar to the mean squared error computed in regression and explicit recommendation models except instead of computing the rating directly, the output from evaluate is computed against a preference which is 1 or 0 depending on if the rating exists or not.
RankingMetrics	setNormalizedDiscountedCumulativeGain(Double normalizedDiscountedCumulativeGain) A metric to determine the goodness of a ranking calculated from the predicted confidence by comparing it to an ideal rank measured by the original ratings.

Methods inherited from class com.google.api.client.json.GenericJson

getFactory, setFactory, toPrettyString, toString

Methods inherited from class com.google.api.client.util.GenericData

entrySet, equals, get, getClassInfo, getUnknownKeys, hashCode, put, putAll, remove, setUnknownKeys

Methods inherited from class java.util.AbstractMap

clear, containsKey, containsValue, isEmpty, keySet, size, values

Methods inherited from class java.lang.Object

finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface java.util.Map

compute, computeIfAbsent, computeIfPresent, forEach, getOrDefault, merge, putIfAbsent, remove, replace, replace, replaceAll

Constructor Detail

RankingMetrics

public RankingMetrics()

Method Detail

getAverageRank

public Double getAverageRank()

Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.

Returns:

value or null for none

setAverageRank

public RankingMetrics setAverageRank(Double averageRank)

Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.

Parameters:

averageRank - averageRank or null for none

getMeanAveragePrecision

public Double getMeanAveragePrecision()

Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.

Returns:

value or null for none

setMeanAveragePrecision

public RankingMetrics setMeanAveragePrecision(Double meanAveragePrecision)

Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.

Parameters:

meanAveragePrecision - meanAveragePrecision or null for none

getMeanSquaredError

public Double getMeanSquaredError()

Similar to the mean squared error computed in regression and explicit recommendation models except instead of computing the rating directly, the output from evaluate is computed against a preference which is 1 or 0 depending on if the rating exists or not.

Returns:

value or null for none

setMeanSquaredError

public RankingMetrics setMeanSquaredError(Double meanSquaredError)

Similar to the mean squared error computed in regression and explicit recommendation models except instead of computing the rating directly, the output from evaluate is computed against a preference which is 1 or 0 depending on if the rating exists or not.

Parameters:

meanSquaredError - meanSquaredError or null for none

getNormalizedDiscountedCumulativeGain

public Double getNormalizedDiscountedCumulativeGain()

A metric to determine the goodness of a ranking calculated from the predicted confidence by comparing it to an ideal rank measured by the original ratings.

Returns:

value or null for none

setNormalizedDiscountedCumulativeGain

public RankingMetrics setNormalizedDiscountedCumulativeGain(Double normalizedDiscountedCumulativeGain)

A metric to determine the goodness of a ranking calculated from the predicted confidence by comparing it to an ideal rank measured by the original ratings.

Parameters:

normalizedDiscountedCumulativeGain - normalizedDiscountedCumulativeGain or null for none

set

public RankingMetrics set(String fieldName,
                          Object value)

Overrides:

set in class com.google.api.client.json.GenericJson

clone

public RankingMetrics clone()

Overrides:

clone in class com.google.api.client.json.GenericJson