com.google.api.services.discoveryengine.v1.model

Class GoogleCloudDiscoveryengineV1alphaQualityMetrics

java.lang.Object

java.util.AbstractMap<String,Object>

com.google.api.client.util.GenericData

com.google.api.client.json.GenericJson

com.google.api.services.discoveryengine.v1.model.GoogleCloudDiscoveryengineV1alphaQualityMetrics

All Implemented Interfaces:

Cloneable, Map<String,Object>

public final class GoogleCloudDiscoveryengineV1alphaQualityMetrics
extends com.google.api.client.json.GenericJson

Describes the metrics produced by the evaluation.

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 Discovery Engine 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
GoogleCloudDiscoveryengineV1alphaQualityMetrics()

Method Summary

Modifier and Type	Method and Description
GoogleCloudDiscoveryengineV1alphaQualityMetrics	clone()
GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics	getDocNdcg() Normalized discounted cumulative gain (NDCG) per document, at various top-k cutoff levels.
GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics	getDocPrecision() Precision per document, at various top-k cutoff levels.
GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics	getDocRecall() Recall per document, at various top-k cutoff levels.
GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics	getPageNdcg() Normalized discounted cumulative gain (NDCG) per page, at various top-k cutoff levels.
GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics	getPageRecall() Recall per page, at various top-k cutoff levels.
GoogleCloudDiscoveryengineV1alphaQualityMetrics	set(String fieldName, Object value)
GoogleCloudDiscoveryengineV1alphaQualityMetrics	setDocNdcg(GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics docNdcg) Normalized discounted cumulative gain (NDCG) per document, at various top-k cutoff levels.
GoogleCloudDiscoveryengineV1alphaQualityMetrics	setDocPrecision(GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics docPrecision) Precision per document, at various top-k cutoff levels.
GoogleCloudDiscoveryengineV1alphaQualityMetrics	setDocRecall(GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics docRecall) Recall per document, at various top-k cutoff levels.
GoogleCloudDiscoveryengineV1alphaQualityMetrics	setPageNdcg(GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics pageNdcg) Normalized discounted cumulative gain (NDCG) per page, at various top-k cutoff levels.
GoogleCloudDiscoveryengineV1alphaQualityMetrics	setPageRecall(GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics pageRecall) Recall per page, at various top-k cutoff levels.

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

GoogleCloudDiscoveryengineV1alphaQualityMetrics

public GoogleCloudDiscoveryengineV1alphaQualityMetrics()

Method Detail

getDocNdcg

public GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics getDocNdcg()

Normalized discounted cumulative gain (NDCG) per document, at various top-k cutoff levels. NDCG measures the ranking quality, giving higher relevance to top results. Example (top-3): Suppose SampleQuery with three retrieved documents (D1, D2, D3) and binary relevance judgements (1 for relevant, 0 for not relevant): Retrieved: [D3 (0), D1 (1), D2 (1)] Ideal: [D1 (1), D2 (1), D3 (0)] Calculate NDCG@3 for each SampleQuery: * DCG@3: 0/log2(1+1) + 1/log2(2+1) + 1/log2(3+1) = 1.13 * Ideal DCG@3: 1/log2(1+1) + 1/log2(2+1) + 0/log2(3+1) = 1.63 * NDCG@3: 1.13/1.63 = 0.693

Returns:

value or null for none

setDocNdcg

public GoogleCloudDiscoveryengineV1alphaQualityMetrics setDocNdcg(GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics docNdcg)

Normalized discounted cumulative gain (NDCG) per document, at various top-k cutoff levels. NDCG measures the ranking quality, giving higher relevance to top results. Example (top-3): Suppose SampleQuery with three retrieved documents (D1, D2, D3) and binary relevance judgements (1 for relevant, 0 for not relevant): Retrieved: [D3 (0), D1 (1), D2 (1)] Ideal: [D1 (1), D2 (1), D3 (0)] Calculate NDCG@3 for each SampleQuery: * DCG@3: 0/log2(1+1) + 1/log2(2+1) + 1/log2(3+1) = 1.13 * Ideal DCG@3: 1/log2(1+1) + 1/log2(2+1) + 0/log2(3+1) = 1.63 * NDCG@3: 1.13/1.63 = 0.693

Parameters:

docNdcg - docNdcg or null for none

getDocPrecision

public GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics getDocPrecision()

Precision per document, at various top-k cutoff levels. Precision is the fraction of retrieved documents that are relevant. Example (top-5): * For a single SampleQuery, If 4 out of 5 retrieved documents in the top-5 are relevant, precision@5 = 4/5 = 0.8

Returns:

value or null for none

setDocPrecision

public GoogleCloudDiscoveryengineV1alphaQualityMetrics setDocPrecision(GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics docPrecision)

Precision per document, at various top-k cutoff levels. Precision is the fraction of retrieved documents that are relevant. Example (top-5): * For a single SampleQuery, If 4 out of 5 retrieved documents in the top-5 are relevant, precision@5 = 4/5 = 0.8

Parameters:

docPrecision - docPrecision or null for none

getDocRecall

public GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics getDocRecall()

Recall per document, at various top-k cutoff levels. Recall is the fraction of relevant documents retrieved out of all relevant documents. Example (top-5): * For a single SampleQuery, If 3 out of 5 relevant documents are retrieved in the top-5, recall@5 = 3/5 = 0.6

Returns:

value or null for none

setDocRecall

public GoogleCloudDiscoveryengineV1alphaQualityMetrics setDocRecall(GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics docRecall)

Recall per document, at various top-k cutoff levels. Recall is the fraction of relevant documents retrieved out of all relevant documents. Example (top-5): * For a single SampleQuery, If 3 out of 5 relevant documents are retrieved in the top-5, recall@5 = 3/5 = 0.6

Parameters:

docRecall - docRecall or null for none

getPageNdcg

public GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics getPageNdcg()

Normalized discounted cumulative gain (NDCG) per page, at various top-k cutoff levels. NDCG measures the ranking quality, giving higher relevance to top results. Example (top-3): Suppose SampleQuery with three retrieved pages (P1, P2, P3) and binary relevance judgements (1 for relevant, 0 for not relevant): Retrieved: [P3 (0), P1 (1), P2 (1)] Ideal: [P1 (1), P2 (1), P3 (0)] Calculate NDCG@3 for SampleQuery: * DCG@3: 0/log2(1+1) + 1/log2(2+1) + 1/log2(3+1) = 1.13 * Ideal DCG@3: 1/log2(1+1) + 1/log2(2+1) + 0/log2(3+1) = 1.63 * NDCG@3: 1.13/1.63 = 0.693

Returns:

value or null for none

setPageNdcg

public GoogleCloudDiscoveryengineV1alphaQualityMetrics setPageNdcg(GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics pageNdcg)

Normalized discounted cumulative gain (NDCG) per page, at various top-k cutoff levels. NDCG measures the ranking quality, giving higher relevance to top results. Example (top-3): Suppose SampleQuery with three retrieved pages (P1, P2, P3) and binary relevance judgements (1 for relevant, 0 for not relevant): Retrieved: [P3 (0), P1 (1), P2 (1)] Ideal: [P1 (1), P2 (1), P3 (0)] Calculate NDCG@3 for SampleQuery: * DCG@3: 0/log2(1+1) + 1/log2(2+1) + 1/log2(3+1) = 1.13 * Ideal DCG@3: 1/log2(1+1) + 1/log2(2+1) + 0/log2(3+1) = 1.63 * NDCG@3: 1.13/1.63 = 0.693

Parameters:

pageNdcg - pageNdcg or null for none

getPageRecall

public GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics getPageRecall()

Recall per page, at various top-k cutoff levels. Recall is the fraction of relevant pages retrieved out of all relevant pages. Example (top-5): * For a single SampleQuery, if 3 out of 5 relevant pages are retrieved in the top-5, recall@5 = 3/5 = 0.6

Returns:

value or null for none

setPageRecall

public GoogleCloudDiscoveryengineV1alphaQualityMetrics setPageRecall(GoogleCloudDiscoveryengineV1alphaQualityMetricsTopkMetrics pageRecall)

Recall per page, at various top-k cutoff levels. Recall is the fraction of relevant pages retrieved out of all relevant pages. Example (top-5): * For a single SampleQuery, if 3 out of 5 relevant pages are retrieved in the top-5, recall@5 = 3/5 = 0.6

Parameters:

pageRecall - pageRecall or null for none

set

public GoogleCloudDiscoveryengineV1alphaQualityMetrics set(String fieldName,
                                                           Object value)

Overrides:

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

clone

public GoogleCloudDiscoveryengineV1alphaQualityMetrics clone()

Overrides:

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