Class QualityMetrics (0.12.2)

Version 0.12.2

• 0.13.5 (latest)
• 0.13.4
• 0.12.3
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• 0.3.1
• 0.2.1
• 0.1.1

QualityMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Describes the metrics produced by the evaluation.

Attributes
Name	Description
doc_recall	google.cloud.discoveryengine_v1alpha.types.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_precision	google.cloud.discoveryengine_v1alpha.types.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_ndcg	google.cloud.discoveryengine_v1alpha.types.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
page_recall	google.cloud.discoveryengine_v1alpha.types.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_ndcg	google.cloud.discoveryengine_v1alpha.types.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

Classes

TopkMetrics

TopkMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Stores the metric values at specific top-k levels.