rank_documents
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
rank_documents has 34 facts recorded in Dontopedia across 4 references, with 5 live disagreements.
Mostly:has parameter(6), is tested by(4), rdf:type(3)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (14)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
testsFunctionTests Function(4)
- Test Empty Arrays
ex:test-empty-arrays - Test Large Arrays
ex:test-large-arrays - Test Mismatched Dimensions
ex:test-mismatched-dimensions - Test Single Element Arrays
ex:test-single-element-arrays
isParameterOfIs Parameter of(3)
- Dense Scores I
ex:dense-scores-i - Query
ex:query - Sparse Scores I
ex:sparse-scores-i
assignedByAssigned by(1)
- Prediction
ex:prediction
containsContains(1)
- Python Code
ex:python-code
containsStepContains Step(1)
- Code Execution Order
ex:code-execution-order
describesDescribes(1)
- Input Validation
ex:input-validation
encapsulatesTestsForEncapsulates Tests for(1)
- Test Class
ex:test-class
hasStepHas Step(1)
- Process Sequence
ex:process-sequence
raisedByRaised by(1)
- Value Error
ex:value-error
Other facts (33)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Has Parameter | Query | [2] |
| Has Parameter | Sparse Scores I | [2] |
| Has Parameter | Dense Scores I | [2] |
| Has Parameter | query | [3] |
| Has Parameter | sparse-scores | [3] |
| Has Parameter | dense-scores | [3] |
| Is Tested by | Test Mismatched Dimensions | [3] |
| Is Tested by | Test Empty Arrays | [3] |
| Is Tested by | Test Single Element Arrays | [3] |
| Is Tested by | Test Large Arrays | [3] |
| Rdf:type | Sorting Step | [1] |
| Rdf:type | Function | [2] |
| Rdf:type | Operation | [4] |
| Has Test Coverage | Error Conditions | [3] |
| Has Test Coverage | Edge Cases | [3] |
| Has Test Coverage | Performance Scenarios | [3] |
| Has Behavior | Returns None for Invalid Input | [3] |
| Has Behavior | Returns Ranked Results for Valid Input | [3] |
| Raises Exception | Value Error | [2] |
| Returns None on Mismatched Dimensions | true | [3] |
| Returns None on Empty Arrays | true | [3] |
| Returns Single Element on Single Input | true | [3] |
| Returns Top Ten on Large Input | true | [3] |
| Requires Matching Dimensions | true | [3] |
| Requires Non Empty Arrays | true | [3] |
| Handles Single Element | true | [3] |
| Handles Large Arrays | true | [3] |
| Returns Top N Results | 10 | [3] |
| Has Expected Output Length | 10 | [3] |
| Is Public Function | true | [3] |
| Has Success Test Case | true | [3] |
| Is Unit Under Test | true | [3] |
| Based on | predicted scores | [4] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (4)
ctx:claims/beam/c7de806a-f338-40ff-82dc-3afcd9dc4260- full textbeam-chunktext/plain1 KB
doc:beam/c7de806a-f338-40ff-82dc-3afcd9dc4260Show excerpt
4. **Rank Documents**: Rank the documents based on the combined score \( S_{combined} \). Higher scores indicate more relevant documents. 5. **Evaluate Relevance Lift**: To achieve an 18% relevance lift, you need to ensure that the combine…
ctx:claims/beam/b9f71d2d-9dd8-41f5-a372-36155652965d- full textbeam-chunktext/plain1 KB
doc:beam/b9f71d2d-9dd8-41f5-a372-36155652965dShow excerpt
prediction = rank_documents(query, sparse_scores_i, dense_scores_i) if prediction is not None: predictions.append(prediction) # Evaluate precision true_labels = np.random.randint(0, 2, size=(num_queries, num_documents)) # …
ctx:claims/beam/048ca9bf-98fc-4ca3-8f93-e03d93bedbd6- full textbeam-chunktext/plain1 KB
doc:beam/048ca9bf-98fc-4ca3-8f93-e03d93bedbd6Show excerpt
self.assertEqual(len(result), 10) def test_mismatched_dimensions(self): query = np.random.rand(1000) sparse_scores = np.random.rand(1000) dense_scores = np.random.rand(500) result = rank_document…
ctx:claims/beam/c07ae379-ae89-4db6-8cc7-34e24961d945
See also
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