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public static final class QualityMetrics.Builder extends GeneratedMessageV3.Builder<QualityMetrics.Builder> implements QualityMetricsOrBuilderDescribes the metrics produced by the evaluation.
Protobuf type google.cloud.discoveryengine.v1alpha.QualityMetrics
Inheritance
Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > QualityMetrics.BuilderImplements
QualityMetricsOrBuilderStatic Methods
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()| Returns | |
|---|---|
| Type | Description |
Descriptor |
|
Methods
addRepeatedField(Descriptors.FieldDescriptor field, Object value)
public QualityMetrics.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)| Parameters | |
|---|---|
| Name | Description |
field |
FieldDescriptor |
value |
Object |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
build()
public QualityMetrics build()| Returns | |
|---|---|
| Type | Description |
QualityMetrics |
|
buildPartial()
public QualityMetrics buildPartial()| Returns | |
|---|---|
| Type | Description |
QualityMetrics |
|
clear()
public QualityMetrics.Builder clear()| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
clearDocNdcg()
public QualityMetrics.Builder clearDocNdcg()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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
clearDocPrecision()
public QualityMetrics.Builder clearDocPrecision()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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_precision = 2;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
clearDocRecall()
public QualityMetrics.Builder clearDocRecall()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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_recall = 1;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
clearField(Descriptors.FieldDescriptor field)
public QualityMetrics.Builder clearField(Descriptors.FieldDescriptor field)| Parameter | |
|---|---|
| Name | Description |
field |
FieldDescriptor |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
clearOneof(Descriptors.OneofDescriptor oneof)
public QualityMetrics.Builder clearOneof(Descriptors.OneofDescriptor oneof)| Parameter | |
|---|---|
| Name | Description |
oneof |
OneofDescriptor |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
clearPageNdcg()
public QualityMetrics.Builder clearPageNdcg()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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
clearPageRecall()
public QualityMetrics.Builder clearPageRecall()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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_recall = 4;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
clone()
public QualityMetrics.Builder clone()| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
getDefaultInstanceForType()
public QualityMetrics getDefaultInstanceForType()| Returns | |
|---|---|
| Type | Description |
QualityMetrics |
|
getDescriptorForType()
public Descriptors.Descriptor getDescriptorForType()| Returns | |
|---|---|
| Type | Description |
Descriptor |
|
getDocNdcg()
public 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 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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetrics |
The docNdcg. |
getDocNdcgBuilder()
public QualityMetrics.TopkMetrics.Builder getDocNdcgBuilder()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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetrics.Builder |
|
getDocNdcgOrBuilder()
public 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 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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetricsOrBuilder |
|
getDocPrecision()
public 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, 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 | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetrics |
The docPrecision. |
getDocPrecisionBuilder()
public QualityMetrics.TopkMetrics.Builder getDocPrecisionBuilder()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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_precision = 2;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetrics.Builder |
|
getDocPrecisionOrBuilder()
public 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, 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 | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetricsOrBuilder |
|
getDocRecall()
public 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, 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 | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetrics |
The docRecall. |
getDocRecallBuilder()
public QualityMetrics.TopkMetrics.Builder getDocRecallBuilder()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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_recall = 1;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetrics.Builder |
|
getDocRecallOrBuilder()
public 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, 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 | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetricsOrBuilder |
|
getPageNdcg()
public 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 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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetrics |
The pageNdcg. |
getPageNdcgBuilder()
public QualityMetrics.TopkMetrics.Builder getPageNdcgBuilder()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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetrics.Builder |
|
getPageNdcgOrBuilder()
public 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 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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetricsOrBuilder |
|
getPageRecall()
public 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, 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 | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetrics |
The pageRecall. |
getPageRecallBuilder()
public QualityMetrics.TopkMetrics.Builder getPageRecallBuilder()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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_recall = 4;
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetrics.Builder |
|
getPageRecallOrBuilder()
public 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, 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 | |
|---|---|
| Type | Description |
QualityMetrics.TopkMetricsOrBuilder |
|
hasDocNdcg()
public 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 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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;
| Returns | |
|---|---|
| Type | Description |
boolean |
Whether the docNdcg field is set. |
hasDocPrecision()
public 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, 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 | |
|---|---|
| Type | Description |
boolean |
Whether the docPrecision field is set. |
hasDocRecall()
public 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, 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 | |
|---|---|
| Type | Description |
boolean |
Whether the docRecall field is set. |
hasPageNdcg()
public 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 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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;
| Returns | |
|---|---|
| Type | Description |
boolean |
Whether the pageNdcg field is set. |
hasPageRecall()
public 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, 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 | |
|---|---|
| Type | Description |
boolean |
Whether the pageRecall field is set. |
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()| Returns | |
|---|---|
| Type | Description |
FieldAccessorTable |
|
isInitialized()
public final boolean isInitialized()| Returns | |
|---|---|
| Type | Description |
boolean |
|
mergeDocNdcg(QualityMetrics.TopkMetrics value)
public QualityMetrics.Builder mergeDocNdcg(QualityMetrics.TopkMetrics value)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;
| Parameter | |
|---|---|
| Name | Description |
value |
QualityMetrics.TopkMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
mergeDocPrecision(QualityMetrics.TopkMetrics value)
public QualityMetrics.Builder mergeDocPrecision(QualityMetrics.TopkMetrics value)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_precision = 2;
| Parameter | |
|---|---|
| Name | Description |
value |
QualityMetrics.TopkMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
mergeDocRecall(QualityMetrics.TopkMetrics value)
public QualityMetrics.Builder mergeDocRecall(QualityMetrics.TopkMetrics value)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_recall = 1;
| Parameter | |
|---|---|
| Name | Description |
value |
QualityMetrics.TopkMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
mergeFrom(QualityMetrics other)
public QualityMetrics.Builder mergeFrom(QualityMetrics other)| Parameter | |
|---|---|
| Name | Description |
other |
QualityMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public QualityMetrics.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)| Parameters | |
|---|---|
| Name | Description |
input |
CodedInputStream |
extensionRegistry |
ExtensionRegistryLite |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
| Exceptions | |
|---|---|
| Type | Description |
IOException |
|
mergeFrom(Message other)
public QualityMetrics.Builder mergeFrom(Message other)| Parameter | |
|---|---|
| Name | Description |
other |
Message |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
mergePageNdcg(QualityMetrics.TopkMetrics value)
public QualityMetrics.Builder mergePageNdcg(QualityMetrics.TopkMetrics value)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;
| Parameter | |
|---|---|
| Name | Description |
value |
QualityMetrics.TopkMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
mergePageRecall(QualityMetrics.TopkMetrics value)
public QualityMetrics.Builder mergePageRecall(QualityMetrics.TopkMetrics value)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_recall = 4;
| Parameter | |
|---|---|
| Name | Description |
value |
QualityMetrics.TopkMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
mergeUnknownFields(UnknownFieldSet unknownFields)
public final QualityMetrics.Builder mergeUnknownFields(UnknownFieldSet unknownFields)| Parameter | |
|---|---|
| Name | Description |
unknownFields |
UnknownFieldSet |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setDocNdcg(QualityMetrics.TopkMetrics value)
public QualityMetrics.Builder setDocNdcg(QualityMetrics.TopkMetrics value)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;
| Parameter | |
|---|---|
| Name | Description |
value |
QualityMetrics.TopkMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setDocNdcg(QualityMetrics.TopkMetrics.Builder builderForValue)
public QualityMetrics.Builder setDocNdcg(QualityMetrics.TopkMetrics.Builder builderForValue)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_ndcg = 3;
| Parameter | |
|---|---|
| Name | Description |
builderForValue |
QualityMetrics.TopkMetrics.Builder |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setDocPrecision(QualityMetrics.TopkMetrics value)
public QualityMetrics.Builder setDocPrecision(QualityMetrics.TopkMetrics value)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_precision = 2;
| Parameter | |
|---|---|
| Name | Description |
value |
QualityMetrics.TopkMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setDocPrecision(QualityMetrics.TopkMetrics.Builder builderForValue)
public QualityMetrics.Builder setDocPrecision(QualityMetrics.TopkMetrics.Builder builderForValue)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_precision = 2;
| Parameter | |
|---|---|
| Name | Description |
builderForValue |
QualityMetrics.TopkMetrics.Builder |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setDocRecall(QualityMetrics.TopkMetrics value)
public QualityMetrics.Builder setDocRecall(QualityMetrics.TopkMetrics value)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_recall = 1;
| Parameter | |
|---|---|
| Name | Description |
value |
QualityMetrics.TopkMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setDocRecall(QualityMetrics.TopkMetrics.Builder builderForValue)
public QualityMetrics.Builder setDocRecall(QualityMetrics.TopkMetrics.Builder builderForValue)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics doc_recall = 1;
| Parameter | |
|---|---|
| Name | Description |
builderForValue |
QualityMetrics.TopkMetrics.Builder |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setField(Descriptors.FieldDescriptor field, Object value)
public QualityMetrics.Builder setField(Descriptors.FieldDescriptor field, Object value)| Parameters | |
|---|---|
| Name | Description |
field |
FieldDescriptor |
value |
Object |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setPageNdcg(QualityMetrics.TopkMetrics value)
public QualityMetrics.Builder setPageNdcg(QualityMetrics.TopkMetrics value)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;
| Parameter | |
|---|---|
| Name | Description |
value |
QualityMetrics.TopkMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setPageNdcg(QualityMetrics.TopkMetrics.Builder builderForValue)
public QualityMetrics.Builder setPageNdcg(QualityMetrics.TopkMetrics.Builder builderForValue)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_ndcg = 5;
| Parameter | |
|---|---|
| Name | Description |
builderForValue |
QualityMetrics.TopkMetrics.Builder |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setPageRecall(QualityMetrics.TopkMetrics value)
public QualityMetrics.Builder setPageRecall(QualityMetrics.TopkMetrics value)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_recall = 4;
| Parameter | |
|---|---|
| Name | Description |
value |
QualityMetrics.TopkMetrics |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setPageRecall(QualityMetrics.TopkMetrics.Builder builderForValue)
public QualityMetrics.Builder setPageRecall(QualityMetrics.TopkMetrics.Builder builderForValue)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
.google.cloud.discoveryengine.v1alpha.QualityMetrics.TopkMetrics page_recall = 4;
| Parameter | |
|---|---|
| Name | Description |
builderForValue |
QualityMetrics.TopkMetrics.Builder |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
public QualityMetrics.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)| Parameters | |
|---|---|
| Name | Description |
field |
FieldDescriptor |
index |
int |
value |
Object |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|
setUnknownFields(UnknownFieldSet unknownFields)
public final QualityMetrics.Builder setUnknownFields(UnknownFieldSet unknownFields)| Parameter | |
|---|---|
| Name | Description |
unknownFields |
UnknownFieldSet |
| Returns | |
|---|---|
| Type | Description |
QualityMetrics.Builder |
|