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add python sdk sample (#13338)
* add python sample * add change point * update anomaly detector sample code * update readme * update readme for samples * update MANIFEST.in * fix pr Co-authored-by: yongxingwu <[email protected]> Co-authored-by: Xia Zhu <[email protected]>
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‎sdk/anomalydetector/azure-ai-anomalydetector/MANIFEST.in

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recursive-include tests *.py *.yaml
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recursive-include samples *.py *.md
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include *.md
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include azure/__init__.py
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include azure/ai/__init__.py

‎sdk/anomalydetector/azure-ai-anomalydetector/samples/README.md

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---
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page_type: sample
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languages:
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- python
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products:
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- azure
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- azure-cognitive-services
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- azure-anomaly-detector
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urlFragment: anomalydetector-samples
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---
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# Samples for Azure Anomaly Detector client library for Python
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These code samples show common scenario operations with the Anomaly Detector client library.
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These sample programs show common scenarios for the Anomaly Detector client's offerings.
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|**File Name**|**Description**|
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|----------------|-------------|
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|[sample_detect_entire_series_anomaly.py][sample_detect_entire_series_anomaly] |Detecting anomalies in the entire time series.|
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|[sample_detect_last_point_anomaly.py][sample_detect_last_point_anomaly] |Detecting the anomaly status of the latest data point.|
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|[sample_detect_change_point.py][sample_detect_change_point] |Detecting change points in the entire time series.|
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## Prerequisites
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* Python 2.7 or 3.5 or higher is required to use this package.
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* The Pandas data analysis library.
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* You must have an [Azure subscription][azure_subscription] and an
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[Azure Anomaly Detector account][azure_anomaly_detector_account] to run these samples.
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## Setup
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1. Install the Azure Anomaly Detector client library for Python with [pip][pip]:
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```bash
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pip install azure-ai-anomalydetector
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```
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2. Clone or download this sample repository
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3. Open the sample folder in Visual Studio Code or your IDE of choice.
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## Running the samples
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1. Open a terminal window and `cd` to the directory that the samples are saved in.
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2. Set the environment variables specified in the sample file you wish to run.
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3. Follow the usage described in the file, e.g. `python sample_detect_entire_series_anomaly.py`
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## Next steps
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Check out the [API reference documentation][python-fr-ref-docs] to learn more about
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what you can do with the Azure Anomaly Detector client library.
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[pip]: https://pypi.org/project/pip/
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[azure_subscription]: https://azure.microsoft.com/free/cognitive-services
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[azure_anomaly_detector_account]: https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesAnomalyDetector
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[python-fr-ref-docs]: https://azuresdkdocs.blob.core.windows.net/$web/python/azure-cognitiveservices-anomalydetector/0.3.0/index.html
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[sample_detect_entire_series_anomaly]: ./sample_detect_entire_series_anomaly.py
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[sample_detect_last_point_anomaly]: ./sample_detect_last_point_anomaly.py
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[sample_detect_change_point]: ./sample_detect_change_point.py

‎sdk/anomalydetector/azure-ai-anomalydetector/samples/sample_data/request-data.csv

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2018-03-01T00:00:00Z,32858923
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2018-03-02T00:00:00Z,29615278
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2018-03-03T00:00:00Z,22839355
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2018-03-04T00:00:00Z,25948736
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2018-03-05T00:00:00Z,34139159
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2018-03-06T00:00:00Z,33843985
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2018-03-07T00:00:00Z,33637661
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2018-03-08T00:00:00Z,32627350
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2018-03-09T00:00:00Z,29881076
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2018-03-10T00:00:00Z,22681575
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2018-03-11T00:00:00Z,24629393
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2018-03-12T00:00:00Z,34010679
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2018-03-13T00:00:00Z,33893888
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2018-03-14T00:00:00Z,33760076
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2018-03-15T00:00:00Z,33093515
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2018-03-16T00:00:00Z,29945555
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2018-03-17T00:00:00Z,22676212
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2018-03-18T00:00:00Z,25262514
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2018-03-19T00:00:00Z,33631649
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2018-03-20T00:00:00Z,34468310
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2018-03-21T00:00:00Z,34212281
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2018-03-22T00:00:00Z,38144434
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2018-03-23T00:00:00Z,34662949
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2018-03-24T00:00:00Z,24623684
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2018-03-25T00:00:00Z,26530491
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2018-03-26T00:00:00Z,35445003
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2018-03-27T00:00:00Z,34250789
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2018-03-28T00:00:00Z,33423012
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2018-03-29T00:00:00Z,30744783
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2018-03-30T00:00:00Z,25825128
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2018-03-31T00:00:00Z,21244209
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2018-04-01T00:00:00Z,22576956
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2018-04-02T00:00:00Z,31957221
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2018-04-03T00:00:00Z,33841228
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2018-04-04T00:00:00Z,33554483
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2018-04-05T00:00:00Z,32383350
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2018-04-06T00:00:00Z,29494850
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2018-04-07T00:00:00Z,22815534
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2018-04-08T00:00:00Z,25557267
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2018-04-09T00:00:00Z,34858252
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2018-04-10T00:00:00Z,34750597
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2018-04-11T00:00:00Z,34717956
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2018-04-12T00:00:00Z,34132534
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2018-04-13T00:00:00Z,30762236
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2018-04-14T00:00:00Z,22504059
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2018-04-15T00:00:00Z,26149060
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2018-04-16T00:00:00Z,35250105

‎sdk/anomalydetector/azure-ai-anomalydetector/samples/sample_detect_change_point.py

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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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"""
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FILE: sample_detect_change_point.py
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DESCRIPTION:
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This sample demonstrates how to detect entire series change points.
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Prerequisites:
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* The Anomaly Detector client library for Python
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* A .csv file containing a time-series data set with
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UTC-timestamp and numerical values pairings.
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Example data is included in this repo.
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USAGE:
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python sample_detect_change_point.py
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Set the environment variables with your own values before running the sample:
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1) ANOMALY_DETECTOR_KEY - your source Form Anomaly Detector API key.
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2) ANOMALY_DETECTOR_ENDPOINT - the endpoint to your source Anomaly Detector resource.
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"""
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import os
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from azure.ai.anomalydetector import AnomalyDetectorClient
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from azure.ai.anomalydetector.models import DetectRequest, TimeSeriesPoint, TimeGranularity, \
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AnomalyDetectorError
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from azure.core.credentials import AzureKeyCredential
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import pandas as pd
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class DetectChangePointsSample(object):
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def detect_change_point(self):
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SUBSCRIPTION_KEY = os.environ["ANOMALY_DETECTOR_KEY"]
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ANOMALY_DETECTOR_ENDPOINT = os.environ["ANOMALY_DETECTOR_ENDPOINT"]
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TIME_SERIES_DATA_PATH = os.path.join("./sample_data", "request-data.csv")
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# Create an Anomaly Detector client
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# <client>
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client = AnomalyDetectorClient(AzureKeyCredential(SUBSCRIPTION_KEY), ANOMALY_DETECTOR_ENDPOINT)
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# </client>
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# Load in the time series data file
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# <loadDataFile>
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series = []
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data_file = pd.read_csv(TIME_SERIES_DATA_PATH, header=None, encoding='utf-8', parse_dates=[0])
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for index, row in data_file.iterrows():
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series.append(TimeSeriesPoint(timestamp=row[0], value=row[1]))
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# </loadDataFile>
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# Create a request from the data file
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# <request>
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request = DetectRequest(series=series, granularity=TimeGranularity.daily)
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# </request>
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# detect change points throughout the entire time series
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# <detectChangePoint>
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print('Detecting change points in the entire time series.')
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try:
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response = client.detect_change_point(request)
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except AnomalyDetectorError as e:
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print('Error code: {}'.format(e.error.code), 'Error message: {}'.format(e.error.message))
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except Exception as e:
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print(e)
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if any(response.is_change_point):
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print('An change point was detected at index:')
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for i, value in enumerate(response.is_change_point):
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if value:
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print(i)
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else:
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print('No change point were detected in the time series.')
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# </detectChangePoint>
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if __name__ == '__main__':
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sample = DetectChangePointsSample()
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sample.detect_change_point()

‎sdk/anomalydetector/azure-ai-anomalydetector/samples/sample_detect_entire_series_anomaly.py

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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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"""
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FILE: sample_detect_entire_series_anomaly.py
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DESCRIPTION:
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This sample demonstrates how to detect entire series anomalies.
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Prerequisites:
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* The Anomaly Detector client library for Python
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* A .csv file containing a time-series data set with
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UTC-timestamp and numerical values pairings.
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Example data is included in this repo.
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USAGE:
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python sample_detect_entire_series_anomaly.py
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Set the environment variables with your own values before running the sample:
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1) ANOMALY_DETECTOR_KEY - your source Form Anomaly Detector API key.
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2) ANOMALY_DETECTOR_ENDPOINT - the endpoint to your source Anomaly Detector resource.
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"""
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import os
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from azure.ai.anomalydetector import AnomalyDetectorClient
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from azure.ai.anomalydetector.models import DetectRequest, TimeSeriesPoint, TimeGranularity, \
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AnomalyDetectorError
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from azure.core.credentials import AzureKeyCredential
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import pandas as pd
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class DetectEntireAnomalySample(object):
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def detect_entire_series(self):
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SUBSCRIPTION_KEY = os.environ["ANOMALY_DETECTOR_KEY"]
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ANOMALY_DETECTOR_ENDPOINT = os.environ["ANOMALY_DETECTOR_ENDPOINT"]
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TIME_SERIES_DATA_PATH = os.path.join("./sample_data", "request-data.csv")
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# Create an Anomaly Detector client
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# <client>
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client = AnomalyDetectorClient(AzureKeyCredential(SUBSCRIPTION_KEY), ANOMALY_DETECTOR_ENDPOINT)
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# </client>
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# Load in the time series data file
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# <loadDataFile>
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series = []
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data_file = pd.read_csv(TIME_SERIES_DATA_PATH, header=None, encoding='utf-8', parse_dates=[0])
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for index, row in data_file.iterrows():
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series.append(TimeSeriesPoint(timestamp=row[0], value=row[1]))
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# </loadDataFile>
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# Create a request from the data file
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# <request>
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request = DetectRequest(series=series, granularity=TimeGranularity.daily)
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# </request>
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# detect anomalies throughout the entire time series, as a batch
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# <detectAnomaliesBatch>
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print('Detecting anomalies in the entire time series.')
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try:
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response = client.detect_entire_series(request)
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except AnomalyDetectorError as e:
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print('Error code: {}'.format(e.error.code), 'Error message: {}'.format(e.error.message))
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except Exception as e:
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print(e)
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if any(response.is_anomaly):
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print('An anomaly was detected at index:')
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for i, value in enumerate(response.is_anomaly):
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if value:
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print(i)
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else:
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print('No anomalies were detected in the time series.')
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# </detectAnomaliesBatch>
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if __name__ == '__main__':
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sample = DetectEntireAnomalySample()
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sample.detect_entire_series()

‎sdk/anomalydetector/azure-ai-anomalydetector/samples/sample_detect_last_point_anomaly.py

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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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"""
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FILE: sample_detect_last_point_anomaly.py
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DESCRIPTION:
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This sample demonstrates how to detect last series point whether is anomaly.
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Prerequisites:
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* The Anomaly Detector client library for Python
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* A .csv file containing a time-series data set with
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UTC-timestamp and numerical values pairings.
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Example data is included in this repo.
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USAGE:
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python sample_detect_last_point_anomaly.py
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Set the environment variables with your own values before running the sample:
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1) ANOMALY_DETECTOR_KEY - your source Form Anomaly Detector API key.
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2) ANOMALY_DETECTOR_ENDPOINT - the endpoint to your source Anomaly Detector resource.
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"""
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import os
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from azure.ai.anomalydetector import AnomalyDetectorClient
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from azure.ai.anomalydetector.models import DetectRequest, TimeSeriesPoint, TimeGranularity, \
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AnomalyDetectorError
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from azure.core.credentials import AzureKeyCredential
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import pandas as pd
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class DetectLastAnomalySample(object):
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def detect_last_point(self):
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SUBSCRIPTION_KEY = os.environ["ANOMALY_DETECTOR_KEY"]
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ANOMALY_DETECTOR_ENDPOINT = os.environ["ANOMALY_DETECTOR_ENDPOINT"]
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TIME_SERIES_DATA_PATH = os.path.join("./sample_data", "request-data.csv")
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# Create an Anomaly Detector client
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# <client>
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client = AnomalyDetectorClient(AzureKeyCredential(SUBSCRIPTION_KEY), ANOMALY_DETECTOR_ENDPOINT)
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# </client>
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# Load in the time series data file
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# <loadDataFile>
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series = []
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data_file = pd.read_csv(TIME_SERIES_DATA_PATH, header=None, encoding='utf-8', parse_dates=[0])
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for index, row in data_file.iterrows():
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series.append(TimeSeriesPoint(timestamp=row[0], value=row[1]))
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# </loadDataFile>
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# Create a request from the data file
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# <request>
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request = DetectRequest(series=series, granularity=TimeGranularity.daily)
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# </request>
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# Detect the anomaly status of the latest data point
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# <latestPointDetection>
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print('Detecting the anomaly status of the latest data point.')
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try:
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response = client.detect_last_point(request)
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except AnomalyDetectorError as e:
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print('Error code: {}'.format(e.error.code), 'Error message: {}'.format(e.error.message))
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except Exception as e:
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print(e)
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if response.is_anomaly:
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print('The latest point is detected as anomaly.')
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else:
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print('The latest point is not detected as anomaly.')
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# </latestPointDetection>
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if __name__ == '__main__':
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sample = DetectLastAnomalySample()
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sample.detect_last_point()

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