使用隔离林进行多变量异常情况检测
本文介绍了如何在 Apache Spark 上使用 SynapseML 进行多变量异常情况检测。 通过多变量异常情况检测，可以检测多个变量或时序中的异常情况，同时考虑到不同变量之间的所有相互关联和依赖关系。 在此方案中，我们使用 SynapseML 训练一个隔离林模型来进行多变量异常情况检测，然后使用已训练的模型来推断包含来自三个 IoT 传感器的合成度量的数据集中的多变量异常情况。
若要详细了解隔离林模型，请查看 Liu 等人编写的原始论文。
先决条件
- 将笔记本附加到湖屋。 在左侧，选择“添加”来添加现有湖屋，或者创建湖屋。
库导入
from IPython import get_ipython
from IPython.terminal.interactiveshell import TerminalInteractiveShell
import uuid
import mlflow
from pyspark.sql import functions as F
from pyspark.ml.feature import VectorAssembler
from pyspark.sql.types import *
from pyspark.ml import Pipeline
from synapse.ml.isolationforest import *
from synapse.ml.explainers import *
%matplotlib inline
from pyspark.sql import SparkSession
# Bootstrap Spark Session
spark = SparkSession.builder.getOrCreate()
from synapse.ml.core.platform import *
if running_on_synapse():
shell = TerminalInteractiveShell.instance()
shell.define_macro("foo", """a,b=10,20""")
输入数据
# Table inputs
timestampColumn = "timestamp" # str: the name of the timestamp column in the table
inputCols = [
"sensor_1",
"sensor_2",
"sensor_3",
] # list(str): the names of the input variables
# Training Start time, and number of days to use for training:
trainingStartTime = (
"2022-02-24T06:00:00Z" # datetime: datetime for when to start the training
)
trainingEndTime = (
"2022-03-08T23:55:00Z" # datetime: datetime for when to end the training
)
inferenceStartTime = (
"2022-03-09T09:30:00Z" # datetime: datetime for when to start the training
)
inferenceEndTime = (
"2022-03-20T23:55:00Z" # datetime: datetime for when to end the training
)
# Isolation Forest parameters
contamination = 0.021
num_estimators = 100
max_samples = 256
max_features = 1.0
读取数据
df = (
spark.read.format("csv")
.option("header", "true")
.load(
"wasbs://publicwasb@mmlspark.blob.core.windows.net/generated_sample_mvad_data.csv"
)
)
将列强制转换为适当的数据类型
df = (
df.orderBy(timestampColumn)
.withColumn("timestamp", F.date_format(timestampColumn, "yyyy-MM-dd'T'HH:mm:ss'Z'"))
.withColumn("sensor_1", F.col("sensor_1").cast(DoubleType()))
.withColumn("sensor_2", F.col("sensor_2").cast(DoubleType()))
.withColumn("sensor_3", F.col("sensor_3").cast(DoubleType()))
.drop("_c5")
)
display(df)
训练数据准备
# filter to data with timestamps within the training window
df_train = df.filter(
(F.col(timestampColumn) >= trainingStartTime)
& (F.col(timestampColumn) <= trainingEndTime)
)
display(df_train)
测试数据准备
# filter to data with timestamps within the inference window
df_test = df.filter(
(F.col(timestampColumn) >= inferenceStartTime)
& (F.col(timestampColumn) <= inferenceEndTime)
)
display(df_test)
训练隔离林模型
isolationForest = (
IsolationForest()
.setNumEstimators(num_estimators)
.setBootstrap(False)
.setMaxSamples(max_samples)
.setMaxFeatures(max_features)
.setFeaturesCol("features")
.setPredictionCol("predictedLabel")
.setScoreCol("outlierScore")
.setContamination(contamination)
.setContaminationError(0.01 * contamination)
.setRandomSeed(1)
)
接下来，我们将创建一个 ML 管道来训练隔离林模型。 我们还演示如何创建 MLflow 试验并注册已训练的模型。
仅当稍后访问已训练的模型时，才需要 MLflow 模型注册。 对于训练模型并在同一笔记本中执行推理，模型对象模型就已足够。
va = VectorAssembler(inputCols=inputCols, outputCol="features")
pipeline = Pipeline(stages=[va, isolationForest])
model = pipeline.fit(df_train)
执行推理
加载已训练的隔离林模型
执行推理
df_test_pred = model.transform(df_test)
display(df_test_pred)