RandomCutForestConfiguration ¶

class RandomCutForestConfiguration(TypedDict, closed=True):
    query: "capo_amp.types.random_cut_forest_query.RandomCutForestQuery"
    """<p>The Prometheus query used to retrieve the time-series data for anomaly detection.</p> <important> <p>Random Cut Forest queries must be wrapped by a supported PromQL aggregation operator. For more information, see <a href="https://prometheus.io/docs/prometheus/latest/querying/operators/#aggregation-operators">Aggregation operators</a> on the <i>Prometheus docs</i> website.</p> <p> <b>Supported PromQL aggregation operators</b>: <code>avg</code>, <code>count</code>, <code>group</code>, <code>max</code>, <code>min</code>, <code>quantile</code>, <code>stddev</code>, <code>stdvar</code>, and <code>sum</code>.</p> </important>"""
    shingle_size: "int"
    """<p>The number of consecutive data points used to create a shingle for the Random Cut Forest algorithm. The default number is 8 consecutive data points.</p>"""
    sample_size: "int"
    """<p>The number of data points sampled from the input stream for the Random Cut Forest algorithm. The default number is 256 consecutive data points.</p>"""
    ignore_near_expected_from_above: NotRequired[
        "capo_amp.types.ignore_near_expected.IgnoreNearExpected"
    ]
    """<p>Configuration for ignoring values that are near expected values from above during anomaly detection.</p>"""
    ignore_near_expected_from_below: NotRequired[
        "capo_amp.types.ignore_near_expected.IgnoreNearExpected"
    ]
    """<p>Configuration for ignoring values that are near expected values from below during anomaly detection.</p>"""