Python clx.analytics.anomaly_detection.dbscan用法及代码示例

用法: clx.analytics.anomaly_detection.dbscan(feature_dataframe, min_samples=3, eps=0.3)

将特征 DataFrame 传递给此函数以检测特征 DataFrame 中的异常。该函数使用cuML DBSCAN 检测异常并输出相关标签 0,1,-1。

参数：

:param feature_dataframe: Feature dataframe to be used for clustering：
:type feature_dataframe: cudf.DataFrame：
:param min_samples: Minimum samples to use for dbscan：
:type min_samples: int：
:param eps: Max distance to use for dbscan：
:type eps: float：

例子：

>>> import cudf
>>> import clx.features
>>> import clx.analytics.anomaly_detection
>>> df = cudf.DataFrame(
>>>         {
>>>             "time": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
>>>             "user": ["u1","u1","u1","u1","u1","u1","u1","u1","u1","u1","u5","u4","u2","u3"],
>>>             "computer": ["c1","c2","c3","c1","c2","c3","c1","c1","c2","c3","c1","c1","c5","c6"],
>>>         }
>>>     )
>>> feature_df = clx.features.frequency(df, entity_id="user", feature_id="computer")
>>> labels = clx.analytics.anomaly_detection.dbscan(feature_df, min_samples=2, eps=0.5)
>>> labels
    0   -1
    1   -1
    2   -1
    dtype: int32

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注：本文由纯净天空筛选整理自rapids.ai大神的英文原创作品 clx.analytics.anomaly_detection.dbscan。非经特殊声明，原始代码版权归原作者所有，本译文未经允许或授权，请勿转载或复制。