wehub-resource-sync
30 lines
1.1 KiB
ReStructuredText
30 lines
1.1 KiB
ReStructuredText
Generating Cluster IDs
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======================
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The underperforming group issue manager provides the option for passing pre-computed
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cluster IDs to `find_issues`. These cluster IDs can be obtained by clustering
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the features using algorithms such as K-Means, DBSCAN, HDBSCAN etc. Note that
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* K-Means requires specifying the number of clusters explicitly.
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* DBSCAN is sensitive to the choice of `eps` (radius) and `min_samples` (minimum samples for each cluster).
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Example:
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.. code-block:: python
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import datalab
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from sklearn.cluster import KMeans
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features, labels = your_data() # Get features and labels
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pred_probs = get_pred_probs() # Get prediction probabilities for all samples
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# Group features into 8 clusters
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clusterer = KMeans(n_clusters=5)
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clusterer.fit(features)
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cluster_ids = clusterer.labels_
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lab = Datalab(data={"features": features, "y": labels}, label_name="y")
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issue_types = {"underperforming_group": {"cluster_ids": cluster_ids}}
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lab.find_issues(features=features, pred_probs=pred_probs, issue_types=issue_types)
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