Discovery Engine V1BETA API - Class Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics (v0.14.0)

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Reference documentation and code samples for the Discovery Engine V1BETA API class Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics.

Describes the metrics produced by the evaluation.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#doc_ndcg

def doc_ndcg() -> ::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics

Returns

• (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

#doc_ndcg=

def doc_ndcg=(value) -> ::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics

Parameter

• value (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

• (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

#doc_precision

def doc_precision() -> ::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics

Returns

• (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

#doc_precision=

def doc_precision=(value) -> ::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics

Parameter

• value (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

• (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

#doc_recall

def doc_recall() -> ::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics

Returns

• (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

#doc_recall=

def doc_recall=(value) -> ::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics

Parameter

• value (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

• (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

#page_ndcg

def page_ndcg() -> ::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics

Returns

• (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

#page_ndcg=

def page_ndcg=(value) -> ::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics

Parameter

• value (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

• (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

#page_recall

def page_recall() -> ::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics

Returns

• (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

#page_recall=

def page_recall=(value) -> ::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics

Parameter

• value (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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

• (::Google::Cloud::DiscoveryEngine::V1beta::QualityMetrics::TopkMetrics) —

  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