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public interface QualityMetricsOrBuilder extends MessageOrBuilderImplements
MessageOrBuilderMethods
getDocNdcg()
public abstract 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. |
getDocNdcgOrBuilder()
public abstract 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 abstract 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. |
getDocPrecisionOrBuilder()
public abstract 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 abstract 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. |
getDocRecallOrBuilder()
public abstract 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 abstract 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. |
getPageNdcgOrBuilder()
public abstract 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 abstract 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. |
getPageRecallOrBuilder()
public abstract 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 abstract 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 abstract 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 abstract 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 abstract 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 abstract 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. |