Create an Anomaly Detection Workflow#

[8]:

# Import to be able to import python package from src
import sys
sys.path.insert(0, '../src')

[9]:

import pandas as pd
import numpy as np
import ontime as on
from IPython.display import Image

Prerequisite#

Install Prefect within your project

[10]:

!pip install prefect

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[notice] A new release of pip is available: 24.1 -> 24.3.1
[notice] To update, run: pip install --upgrade pip

Generation of random time series#

[11]:

ts = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))

Create a Prefect Flow and Tasks#

[12]:

from prefect import task, flow, get_run_logger
from ontime.context import common

@task
def task_detect(ts):
    det = on.detectors.quantile(low_quantile=0.1, high_quantile=0.9)
    det.fit(ts)
    return det.detect(ts)

@task
def task_predict(ts):
    model = common.GenericPredictor()
    model.fit(ts)
    return model.predict(48)

@flow
def detection_flow(ts, log_prints=True):
    ts_ano = task_detect(ts)
    ts_pred = task_predict(ts)
    ts_pred_ano = task_detect(ts_pred)
    return ts_ano, ts_pred, ts_pred_ano

Run the flow#

[14]:

ts_ano, ts_pred, ts_pred_ano = detection_flow(ts)

10:05:21.293 | INFO    | Flow run 'tangible-elephant' - Beginning flow run 'tangible-elephant' for flow 'detection-flow'

10:05:21.381 | INFO    | Task run 'task_detect-48e' - Finished in state Completed()

10:05:22.481 | INFO    | Task run 'task_predict-70b' - Finished in state Completed()

10:05:22.556 | INFO    | Task run 'task_detect-33f' - Finished in state Completed()

10:05:22.611 | INFO    | Flow run 'tangible-elephant' - Finished in state Completed()

Show results#

[18]:

(
    on.Plot()
    .add(on.marks.line, ts.rename({"random_walk": "truth"}))
    .add(on.marks.line, ts_ano.rename({"random_walk": "anomalies"}))
    .add(on.marks.line, ts_pred.rename({"random_walk": "predictions"}))
    .add(on.marks.line, ts_pred_ano.rename({"random_walk": "predicted anomalies"}))
    .properties(width=500)
    .show()
)

[18]:
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