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RankingMetrics (BigQuery API v2-rev20240905-2.0.0)











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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

All Methods Instance Methods Concrete Methods 

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














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Overview
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Class
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Index
Help




Prev Class
Next Class


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Summary: 
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Copyright © 2011–2024 Google. All rights reserved.

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.