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Represents custom features derived from multiple columns using clustering analysis. Multivariate features combine multiple numeric columns into a single derived feature using k-means clustering algorithms. This enables monitoring of multivariate drift and detecting unusual combinations that might not be apparent when monitoring columns individually. The feature type is automatically set to CustomFeatureType.FROM_COLUMNS and uses clustering to group similar combinations of column values for drift detection.

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Examples

Creating a user behavior multivariate feature: 
behavior_feature = Multivariate(
    name="user_engagement_cluster",
    columns=["page_views", "session_duration", "clicks"],
    n_clusters=8,
    monitor_components=True
)
Creating a system performance multivariate feature: 
perf_feature = Multivariate(
    name="system_health",
    columns=["cpu_usage", "memory_usage", "response_time"],
    n_clusters=5,
    monitor_components=False
)

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type

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n_clusters

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centroids

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columns

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monitor_components

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classmethod validate_columns()

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Returns

List[str]

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classmethod validate_n_clusters()

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Returns

int

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model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].