com.google.cloud.discoveryengine.v1alpha

Interface QualityMetricsOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

QualityMetrics, QualityMetrics.Builder

public interface QualityMetricsOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
QualityMetrics.TopkMetrics	getDocNdcg() Normalized discounted cumulative gain (NDCG) per document, at various top-k cutoff levels.
QualityMetrics.TopkMetricsOrBuilder	getDocNdcgOrBuilder() Normalized discounted cumulative gain (NDCG) per document, at various top-k cutoff levels.
QualityMetrics.TopkMetrics	getDocPrecision() Precision per document, at various top-k cutoff levels.
QualityMetrics.TopkMetricsOrBuilder	getDocPrecisionOrBuilder() Precision per document, at various top-k cutoff levels.
QualityMetrics.TopkMetrics	getDocRecall() Recall per document, at various top-k cutoff levels.
QualityMetrics.TopkMetricsOrBuilder	getDocRecallOrBuilder() Recall per document, at various top-k cutoff levels.
QualityMetrics.TopkMetrics	getPageNdcg() Normalized discounted cumulative gain (NDCG) per page, at various top-k cutoff levels.
QualityMetrics.TopkMetricsOrBuilder	getPageNdcgOrBuilder() Normalized discounted cumulative gain (NDCG) per page, at various top-k cutoff levels.
QualityMetrics.TopkMetrics	getPageRecall() Recall per page, at various top-k cutoff levels.
QualityMetrics.TopkMetricsOrBuilder	getPageRecallOrBuilder() Recall per page, at various top-k cutoff levels.
boolean	hasDocNdcg() Normalized discounted cumulative gain (NDCG) per document, at various top-k cutoff levels.
boolean	hasDocPrecision() Precision per document, at various top-k cutoff levels.
boolean	hasDocRecall() Recall per document, at various top-k cutoff levels.
boolean	hasPageNdcg() Normalized discounted cumulative gain (NDCG) per page, at various top-k cutoff levels.
boolean	hasPageRecall() Recall per page, at various top-k cutoff levels.

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

hasDocRecall

boolean hasDocRecall()

 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][google.cloud.discoveryengine.v1alpha.SampleQuery], If 3 out
  of 5 relevant documents are retrieved in the top-5, recall@5 = 3/5 = 0.6
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_recall = 1;

Returns:

Whether the docRecall field is set.

getDocRecall

QualityMetrics.TopkMetrics 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][google.cloud.discoveryengine.v1alpha.SampleQuery], If 3 out
  of 5 relevant documents are retrieved in the top-5, recall@5 = 3/5 = 0.6
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_recall = 1;

Returns:

The docRecall.

getDocRecallOrBuilder

QualityMetrics.TopkMetricsOrBuilder getDocRecallOrBuilder()

 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][google.cloud.discoveryengine.v1alpha.SampleQuery], If 3 out
  of 5 relevant documents are retrieved in the top-5, recall@5 = 3/5 = 0.6
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_recall = 1;

hasDocPrecision

boolean hasDocPrecision()

 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][google.cloud.discoveryengine.v1alpha.SampleQuery], If 4 out
  of 5 retrieved documents in the top-5 are relevant, precision@5 = 4/5 =
  0.8
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_precision = 2;

Returns:

Whether the docPrecision field is set.

getDocPrecision

QualityMetrics.TopkMetrics 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][google.cloud.discoveryengine.v1alpha.SampleQuery], If 4 out
  of 5 retrieved documents in the top-5 are relevant, precision@5 = 4/5 =
  0.8
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_precision = 2;

Returns:

The docPrecision.

getDocPrecisionOrBuilder

QualityMetrics.TopkMetricsOrBuilder getDocPrecisionOrBuilder()

 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][google.cloud.discoveryengine.v1alpha.SampleQuery], If 4 out
  of 5 retrieved documents in the top-5 are relevant, precision@5 = 4/5 =
  0.8
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_precision = 2;

hasDocNdcg

boolean hasDocNdcg()

 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][google.cloud.discoveryengine.v1alpha.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][google.cloud.discoveryengine.v1alpha.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
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;

Returns:

Whether the docNdcg field is set.

getDocNdcg

QualityMetrics.TopkMetrics 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][google.cloud.discoveryengine.v1alpha.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][google.cloud.discoveryengine.v1alpha.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
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;

Returns:

The docNdcg.

getDocNdcgOrBuilder

QualityMetrics.TopkMetricsOrBuilder getDocNdcgOrBuilder()

 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][google.cloud.discoveryengine.v1alpha.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][google.cloud.discoveryengine.v1alpha.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
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;

hasPageRecall

boolean hasPageRecall()

 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][google.cloud.discoveryengine.v1alpha.SampleQuery], if 3 out
  of 5 relevant pages are retrieved in the top-5, recall@5 = 3/5 = 0.6
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_recall = 4;

Returns:

Whether the pageRecall field is set.

getPageRecall

QualityMetrics.TopkMetrics 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][google.cloud.discoveryengine.v1alpha.SampleQuery], if 3 out
  of 5 relevant pages are retrieved in the top-5, recall@5 = 3/5 = 0.6
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_recall = 4;

Returns:

The pageRecall.

getPageRecallOrBuilder

QualityMetrics.TopkMetricsOrBuilder getPageRecallOrBuilder()

 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][google.cloud.discoveryengine.v1alpha.SampleQuery], if 3 out
  of 5 relevant pages are retrieved in the top-5, recall@5 = 3/5 = 0.6
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_recall = 4;

hasPageNdcg

boolean hasPageNdcg()

 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][google.cloud.discoveryengine.v1alpha.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][google.cloud.discoveryengine.v1alpha.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
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;

Returns:

Whether the pageNdcg field is set.

getPageNdcg

QualityMetrics.TopkMetrics 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][google.cloud.discoveryengine.v1alpha.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][google.cloud.discoveryengine.v1alpha.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
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;

Returns:

The pageNdcg.

getPageNdcgOrBuilder

QualityMetrics.TopkMetricsOrBuilder getPageNdcgOrBuilder()

 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][google.cloud.discoveryengine.v1alpha.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][google.cloud.discoveryengine.v1alpha.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
 

.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;