visualize_correlation
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
visualize_correlation has 25 facts recorded in Dontopedia across 2 references, with 3 live disagreements.
Mostly:calls(5), has parameter(2), compares(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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.
callsFunctionCalls Function(1)
- Code Snippet
ex:code-snippet
containsContains(1)
- Code Module
ex:code-module
demonstratesDemonstrates(1)
- Example Usage
ex:example-usage
hasStepHas Step(1)
- Workflow Sequence
ex:workflow-sequence
Other facts (24)
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 |
|---|---|---|
| Calls | Plt Scatter | [1] |
| Calls | Plt Xlabel | [1] |
| Calls | Plt Ylabel | [1] |
| Calls | Plt Title | [1] |
| Calls | Plt Show | [1] |
| Has Parameter | Ndcg Values | [1] |
| Has Parameter | Map Values | [1] |
| Compares | Ndcg Metric | [1] |
| Compares | Map Metric | [1] |
| Rdf:type | Function | [1] |
| Has Name | visualize_correlation | [1] |
| Described As | Visualize the correlation between NDCG@k and MAP@k. | [1] |
| Part of | Code Module | [1] |
| Called by | Example Usage | [1] |
| Depends on | Calculate Metrics | [1] |
| Used for | Recommendation System Evaluation | [1] |
| Computes | Correlation Plot | [1] |
| Step Order | 2 | [1] |
| Enables | Metric Comparison | [1] |
| Has Complexity | O(n) | [1] |
| Produces | Scatter Plot | [1] |
| Visualizes | Correlation | [2] |
| Uses Visualization Type | Scatter Plot | [2] |
| Plot Type | scatter-plot | [2] |
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 (2)
ctx:claims/beam/120de523-8aa9-44e6-a94f-a9f5d853f0a8- full textbeam-chunktext/plain1 KB
doc:beam/120de523-8aa9-44e6-a94f-a9f5d853f0a8Show excerpt
Here's how you can implement the calculation and visualization: ```python import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import ndcg_score, average_precision_score def calculate_metrics(predictions, labels, k_ndcg…
ctx:claims/beam/7c7c4d94-1626-4327-b6b2-b57b1fc421dd- full textbeam-chunktext/plain1 KB
doc:beam/7c7c4d94-1626-4327-b6b2-b57b1fc421ddShow excerpt
num_queries = 1000 num_items = 10 # Generate random predictions and labels predictions = np.random.rand(num_queries, num_items) labels = np.random.randint(0, 2, size=(num_queries, num_items)) # Calculate metrics for each query ndcg_values…
See also
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