azure-samples / anomalydetector Goto Github PK

Samples for the Anomaly Detection API documentation:

Home Page: https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/

License: MIT License

C# 0.84% Java 0.53% Jupyter Notebook 96.99% Python 1.20% Ruby 0.09% TypeScript 0.10% Go 0.09% JavaScript 0.15%

anomalydetector's Introduction

Anomaly Detector Samples

This repository contains API samples and SDK samples for Anomaly Detector service. Anomaly Detector enables you to monitor and find abnormalities in your time series data by automatically identifying and applying the correct statistical models, regardless of industry, scenario, or data volume.

What's new?

March 2024: Anomaly Detector Now Available on PYPI 🎉

In March 2024, we proudly announce the release of the Anomaly Detector package on PYPI!

While the existing Anomaly Detector as a service will be deprecated by 2026, you can now seamlessly utilize the new package directly on your local machine. No need to create an Azure Anomaly Detector resource—simply install the package and start detecting anomalies right away.

For the latest details and usage instructions, refer to our Python notebook available here: anomaly-detector-pypi-demo.ipynb

👋About Anomaly Detector

Anomaly Detector is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little ML knowledge, either batch validation or real-time inference.

Univariate Anomaly Detection API enables you to monitor and detect abnormalities in your single variable without having to know machine learning. The Anomaly Detector API's algorithms adapt by automatically identifying and applying the best-fitting models to your data, regardless of industry, scenario, or data volume. Using your time series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies.

Multivariate anomaly detection API further enable developers by easily integrating advanced AI for detecting anomalies from groups of metrics, without the need for machine learning knowledge or labeled data. Dependencies and inter-correlations between up to 300 different signals are now automatically counted as key factors. This new capability helps you to proactively protect your complex systems such as software applications, servers, factory machines, spacecraft, or even your business, from failures.

Prerequisites

You must have an Anomaly Detector API resource. Before continuing, you will need the API key and the endpoint from your Azure dashboard.

Or you could create a 7-day free resource of Anomaly Detector from here.

Content

This repository is organized in the following structure, we recommend you go to demo-notebook first to try the simple samples if you are a fan of Python. 🤗

Folder	Description
🆕ipython-notebook	API and SDK sample codes written in python notebook for UVAD adn MVAD. The latest update will start from here first. 😉
sampledata	All the sample datasets that are used in this repository.
sample-multivariate	Sample SDK codes for MVAD(preview version) using 4 languages.
sample-univariate	Sample API and SDK codes for UVAD using 4 languages.
univariate-live-demo	This includes a live demo that you could clone directly and ran on your data or make any modifications.
postman-demo	This includes the tutorial of using postman to trigger Anomaly Detector, which could help better understand from API perspective.

🔗Important links

1️⃣Microsoft Learn - Anomaly Detector

Learning module: Identify abnormal time-series data with Anomaly Detector

2️⃣API/SDK Sample

3️⃣Anomaly Detector in Synapse

4️⃣Anomaly Detector in Azure Databricks

Blog: Detect Anomalies in Equipment with Anomaly Detector in Azure Databricks

5️⃣Anomaly Detector in Azure Data Explorer

6️⃣Anomaly Detector PowerBI

Container demo

(Only support UVAD)

If you want to run the notebook with an on-premise UVAD version of Anomaly Detector as container, there're four prerequisites that must be met:

You have access to the Azure Container Registry which hosts the Anomaly Detector container images. Please complete and submit the Anomaly Detector Container Request form to request access to the container.
You have created an Anomaly Detector resource on Azure.
You have the proper container environment ready to host the Anomaly Detector container. Please read Prerequisites and The host computer for details.
You have Jupyter Notebook installed on your computer. We recommend installing Python and Jupyter using the Anaconda Distribution.

After you pull the container image and spin it up, ensure there's an HTTP endpoint accessible to the APIs and this will be your endpoint for the demo. To run the notebook with your Anomaly Detector container instance, complete the following steps:

Clone this project to your local directory
Start Anaconda Prompt
In the command line, change the working directory to your project directory using cd
Type jupyter notebook and run which opens http://localhost:8888/tree in a browser window
Open one of the notebooks under ipython-notebook folder
Fill in the API key (from your Anomaly Detector resource on Azure) and the endpoint (from your Anomaly Detector container instance)
In the Notebook main menu, click Cell->run all

❤️Support

Need support? Join the Anomaly Detector Community.

anomalydetector's People

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anomalydetector's Issues

Cannot fork the postman collection for Anomaly Detection

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [ ] bug report -> please search issues before submitting
- [ ] feature request
- [X] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

Go to "https://github.com/Azure-Samples/AnomalyDetector/tree/master/postman-demo" and try to download the Postman collection

Any log messages given by the failure

No permission

Expected/desired behavior

I would like to download the collection without any permission issues. Is the collection publicly available?

OS and Version?

Windows 7, 8 or 10. Linux (which distribution). macOS (Yosemite? El Capitan? Sierra?)

Versions

Mention any other details that might be useful

Thanks! We'll be in touch soon.

Anomaly Detection model (demo) fails during the training part

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [x ] bug report -> please search issues before submitting
- [ ] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

upload this data file to blob storage:
test data.zip and run the Multivariate Anomaly Detection model (demo) on it. For some reason, in the training part of the model, it always fails.

Any log messages given by the failure

status of

Expected/desired behavior

The training of the model to success

OS and Version?

Windows 7, 8 or 10. Linux (which distribution). macOS (Yosemite? El Capitan? Sierra?)
macOS Catalina 10.15.7

Versions

Mention any other details that might be useful

Thanks! We'll be in touch soon.

MVAD -SDK-Demo notebook does not work

I have followed the documentation to run the MVAD Python Notebook and it fails with error

ImportError: cannot import name 'DetectionRequest' from 'azure.ai.anomalydetector.models' (c:\Python311\Lib\site-packages\azure\ai\anomalydetector\models\__init__.py)

upon inspection of the file init.py DetectionRequest is indeed missing.

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [x ] bug report -> please search issues before submitting
- [ ] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

Any log messages given by the failure

Run Notebook (Import related packages)

Expected/desired behavior

Imports are imported without error

OS and Version?

Windows 7, 8 or 10. Linux (which distribution). macOS (Yosemite? El Capitan? Sierra?)
Windows 10, Python 3.11

Versions

Mention any other details that might be useful

Thanks! We'll be in touch soon.

Can i do the forecasting on my times series? lets say we have data of last 14 months, can i do the forecast for next month.

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [ ] bug report -> please search issues before submitting
- [x] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

Any log messages given by the failure

Expected/desired behavior

OS and Version?

Windows 7, 8 or 10. Linux (which distribution). macOS (Yosemite? El Capitan? Sierra?)
Window 10

Versions

Mention any other details that might be useful

Thanks! We'll be in touch soon.

Multivariate sample data invalid?

This issue is for a: (mark with an `x`)

- [ ] bug report -> please search issues before submitting
- [ ] feature request
- [x] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

The multivariate sample link at the bottom of this readme is invalid. Furthermore the actual zip file in the multivariate sample data looks like univariate data, what am I missing?

Any log messages given by the failure

N/A

Expected/desired behavior

Sample multivariate data

OS and Version?

N/A

Versions

N/A

Mention any other details that might be useful

Thanks! We'll be in touch soon.

Microsoft Learn Sample Code Link Missing

Please provide us with the following information:

Missing Links on Microsoft Learn Page

Anomaly Detector
There are links missing from above page in the Sample Code section

Minimal steps to reproduce

Anomaly Detector
On the above page

The sample code links have to be changed for languages Python, C#, Java, JavaScript

Expected/desired behavior

For Python link to be added - Python
For C# link to be added - Python
For Javascript link to be added - Python
For Java link to be added - Python

Thanks! We'll be in touch soon.

401 Tier Error for Cognitive Service

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [X] bug report -> please search issues before submitting
- [ ] feature request
- [X] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

Created cognitive search service with S0 tier (only tier I was able to select)

Added api key and endpoint

Ran notebook as is

Any log messages given by the failure

Exception: {"error":{"code":"401","message": "The Find anomalies for the entire series in batch. Operation under Anomaly Detector is not supported with the current subscription key and pricing tier CognitiveServices.S0."}}

There doesn't appear to be any other tier options than S0

Expected/desired behavior

OS and Version?

Windows 10

Versions

Mention any other details that might be useful

Will there be an update to integrate directly with anomaly detector service? Not the anomaly detector within cognitive search service.

Update 'contributors' to 'interpretation' in Visualize Results for Multivariate API Demo Notebook.ipynb

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [ x] bug report -> please search issues before submitting
- [ ] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

Run the Multivariate API Demo Notebook.ipynb notebook

Any log messages given by the failure

Traceback (most recent call last):
File "anomaly_detector/visualize.py", line 183, in
show_contribution(series, raw_result, top_anomaly)
File "anomaly_detector/visualize.py", line 148, in show_contribution
anomaly_result = [
IndexError: list index out of range

Expected/desired behavior

Display plots

OS and Version?

Windows 10 / WSL 2.0

Versions

Mention any other details that might be useful

Current example:

def show_contribution(series, raw_result, anomaly_timestamp):
anomaly_result = [x for x in raw_result['results'] if 'contributors' in x['value'] and x['timestamp'] == top_anomaly][0]
contributors = [x['variable'] for x in anomaly_result['value']['contributors']]

Fixed example:
def show_contribution(series, raw_result, anomaly_timestamp):
anomaly_result = [x for x in raw_result['results'] if 'interpretation' in x['value'] and x['timestamp'] == top_anomaly][0]
contributors = [x['variable'] for x in anomaly_result['value']['interpretation']]

Thanks! We'll be in touch soon.

MVAD-SDK-Demo doesn't work with blob storage URL

- [x ] bug report -> please search issues before submitting
- [ ] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

I followed instructions upto Step 6:Train a model.
The model is created:
Model is created with the modelId: 059dba8c-fa59-11ed-955a-4ece889b01d8

But it fails to train.
Model status: FAILED EpochId: {'epochIds': [], 'trainLosses': [], 'validationLosses': [], 'latenciesInSeconds': []}

"FailToAccessBlobURLError('Please confirm if the blob URL exists, and grant 'Storage Blob Data Reader' role to your Anomaly Detector.', inner_exception=None)"

I'm using a Jupyter Notebook, azure-ai-anomalydetector 3.0.0b6, and have a Storage blob account with the correct permissions set.

Thank you,
Sumeet

InferenceLostConnection error for detect step with more than 5000 data points using Multivariate Anomaly Detection API in Python

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [x] bug report -> please search issues before submitting
- [ ] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

When I submit more than 5,000 data points for the multivariate anomaly detector detect API call https://{endpoint}/multivariate/models/{model_id}/detect), I consistently get the following error. According to the documentation, the maximum number of data points per inference call is 20,000. There is no error when I use less than 5,000 data points for the API call.

Supplying at most 5,000 data points is much more time-consuming because any single detect API call takes around the same time to complete (regardless of the number of data points) in my experience.

Any log messages given by the failure

Error code: InferenceLostConnection. Message: Lost connection with the worker that processed the inference work. This may be caused by service upgrade. Re-trigger inference should help resolve this.

Expected/desired behavior

No error when I supply at most 20,000 data points for a single detect API call.

Mention any other details that might be useful

if True in response.is_anomaly:

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [ x] bug report -> please search issues before submitting
- [ ] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

run the code in VSCOCE

Any log messages given by the failure

Exception has occurred: NameError
name 'response' is not defined
File "C:\vscode\2.py", line 69, in
if True in response.is_anomaly:

Expected/desired behavior

OS and Version?

win 10

Versions

Mention any other details that might be useful

Thanks! We'll be in touch soon.

How to pull out contributors attribute from AnomalyState object

The detect() method in helper script sample_multivariate_detect.py returns a list of AnomalyState objects. I am able to access the timestamp, severity, score, etc. for each timestamp but am unclear on how to grab the contributors list (variables and contribution score) per time stamp. Did not see any documentation for this. Thanks!

Edit: I just saw #47 which suggests it's the interpretation attribute.

Error when running build_figure function

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [X ] bug report -> please search issues before submitting
- [ ] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

Running build_figure on Azure Databricks gives me an error. This error only appears when an anomaly is detected (true value). It seems the API is working but there is an issue with the graphic metrics. Please help.

Any log messages given by the failure

TypeError: '<' not supported between instances of 'numpy.ndarray' and 'str'

Expected/desired behavior

plot graphic

OS and Version?

Windows 7, 8 or 10. Linux (which distribution). macOS (Yosemite? El Capitan? Sierra?)

Versions

Mention any other details that might be useful

The API json returns the following:

{'expectedValues': [32665291.70172784,
33126536.996231694,
33865005.04719163,
34952357.23318992,
35880900.59326456,
37577731.376328364,
38848479.527605504,
39229845.6539477,
39204805.413964026,
39140353.48327427,
39398425.48938306,
39449252.09822685,
40268366.13005982,
40644059.92747147,
40745047.82169999,
40588707.92813035,
40709590.79101679,
41226068.09549811,
41583736.57405577,
42750007.902109794,
43388166.20935269,
43861988.96543184,
43929405.35518512,
44565830.92097248,
45524780.42355837,
46516265.8981908,
47209112.43948283,
47373845.95996373,
47263873.577261515,
46747494.82823343,
46508338.83566142,
46591706.77988794,
46516265.8981908,
47209112.43948283,
47373845.95996373],
'isAnomaly': [False,
False,
False,
True,
True,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
True,
True,
True,
True,
True,
True,
True],
'isNegativeAnomaly': [False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
True,
True,
True],
'isPositiveAnomaly': [False,
False,
False,
True,
True,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
True,
True,
True,
True,
False,
False,
False],
'lowerMargins': [1633264.585086394,
1656326.8498115838,
1693250.2523595802,
1747617.861659497,
1794045.0296632275,
1878886.568816416,
1942423.9763802737,
1961492.282697387,
1960240.2706982046,
1957017.674163714,
1969921.274469152,
1972462.6049113423,
2013418.3065029904,
2032202.9963735715,
2037252.3910849988,
2029435.3964065164,
2035479.5395508409,
2061303.4047749043,
2079186.828702785,
2137500.3951054886,
2169408.3104676306,
2193099.448271595,
2196470.267759256,
2228291.5460486263,
2276239.0211779177,
2325813.294909537,
2360455.6219741404,
2368692.29799819,
2363193.6788630784,
2337374.741411671,
2325416.9417830706,
2329585.3389943987,
2325813.294909537,
2360455.6219741404,
2368692.29799819],
'period': 7,
'upperMargins': [1633264.585086394,
1656326.8498115875,
1693250.252359584,
1747617.861659497,
1794045.0296632275,
1878886.568816416,
1942423.9763802737,
1961492.282697387,
1960240.2706982046,
1957017.674163714,
1969921.274469152,
1972462.6049113423,
2013418.3065029904,
2032202.9963735715,
2037252.3910849988,
2029435.3964065164,
2035479.5395508409,
2061303.4047749043,
2079186.828702785,
2137500.3951054886,
2169408.3104676306,
2193099.448271595,
2196470.267759256,
2228291.5460486263,
2276239.0211779177,
2325813.294909537,
2360455.6219741404,
2368692.29799819,
2363193.6788630784,
2337374.741411671,
2325416.9417830706,
2329585.3389943987,
2325813.294909537,
2360455.6219741404,
2368692.29799819]}

Thanks! We'll be in touch soon.

build_figure is expecting wrong column names

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [ X] bug report -> please search issues before submitting
- [ ] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

run the notebook, try to execute build_figure(sample_data,95)
since the values returned are not in plural there are multiple errors.

Any log messages given by the failure

getting a KeyError when trying build the figure

for example
columns = {'expectedValues': result['expectedValues'],
this is the case for : upperMargins, lowerMargins

Expected/desired behavior

notebook should run w/o errors

OS and Version?

Windows 10.

Versions

Mention any other details that might be useful

running on a notebook vm with Python 3 kernel

Thanks! We'll be in touch soon.

catching classes that do not inherit from BaseException is not allowed/an integer is required

Please provide us with the following information:

This issue is for a: (mark with an `x`)

- [ ] bug report -> please search issues before submitting
- [ ] feature request
- [x] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

Minimal steps to reproduce

https://github.com/Azure-Samples/AnomalyDetector/blob/master/quickstarts/sdk/python-sdk-sample.py

Any log messages given by the failure

Traceback (most recent call last):
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 765, in serialize_data
return self.serialize_type[data_type](data, **kwargs)
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 1117, in serialize_iso
utc = attr.utctimetuple()
File "pandas_libs\tslibs\timestamps.pyx", line 1332, in pandas._libs.tslibs.timestamps.Timestamp.new
TypeError: an integer is required

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:\Users\user\PycharmProjects\pythonProject1\IA Practice\microsoft_azure_anomaly.py", line 32, in
response = client.detect_entire_series(request)
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\azure\ai\anomalydetector\operations_anomaly_detector_client_operations.py", line 71, in detect_entire_series
body_content = self._serialize.body(body, 'DetectRequest')
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 629, in body
return self._serialize(data, data_type, **kwargs)
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 483, in _serialize
return self.serialize_data(
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 783, in serialize_data
return self._serialize(data, **kwargs)
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 523, in _serialize
new_attr = self.serialize_data(orig_attr, attr_desc['type'], **kwargs)
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 775, in serialize_data
return self.serialize_type[iter_type](
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 860, in serialize_iter
serialized.append(self.serialize_data(d, iter_type, **kwargs))
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 783, in serialize_data
return self._serialize(data, **kwargs)
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 523, in _serialize
new_attr = self.serialize_data(orig_attr, attr_desc['type'], **kwargs)
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 780, in serialize_data
raise_with_traceback(
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\exceptions.py", line 51, in raise_with_traceback
raise error.with_traceback(exc_traceback)
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 765, in serialize_data
return self.serialize_type[data_type](data, **kwargs)
File "C:\Users\user\PycharmProjects\pythonProject1\venv\lib\site-packages\msrest\serialization.py", line 1117, in serialize_iso
utc = attr.utctimetuple()
File "pandas_libs\tslibs\timestamps.pyx", line 1332, in pandas._libs.tslibs.timestamps.Timestamp.new
msrest.exceptions.SerializationError: Unable to serialize value: Timestamp('2018-03-01 00:00:00+0000', tz='UTC') as type: 'iso-8601'., TypeError: an integer is required

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:\Users\user\PycharmProjects\pythonProject1\IA Practice\microsoft_azure_anomaly.py", line 33, in
except AnomalyDetectorError as e:
TypeError: catching classes that do not inherit from BaseException is not allowed

Expected/desired behavior

https://github.com/Azure-Samples/AnomalyDetector/blob/master/quickstarts/sdk/python-sdk-sample.py

OS and Version?

Windows 10

Versions

6.6.3

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anomalydetector's Introduction

Anomaly Detector Samples

What's new?

March 2024: Anomaly Detector Now Available on PYPI 🎉

👋About Anomaly Detector

Prerequisites

Content

🔗Important links

1️⃣Microsoft Learn - Anomaly Detector

2️⃣API/SDK Sample

3️⃣Anomaly Detector in Synapse

4️⃣Anomaly Detector in Azure Databricks

5️⃣Anomaly Detector in Azure Data Explorer

6️⃣Anomaly Detector PowerBI

Container demo

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