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| 1 | +# coding=utf-8 |
| 2 | +# -------------------------------------------------------------------------- |
| 3 | +# Copyright (c) Microsoft Corporation. All rights reserved. |
| 4 | +# Licensed under the MIT License. See License.txt in the project root for |
| 5 | +# license information. |
| 6 | +# |
| 7 | +# Code generated by Microsoft (R) AutoRest Code Generator. |
| 8 | +# Changes may cause incorrect behavior and will be lost if the code is |
| 9 | +# regenerated. |
| 10 | +# -------------------------------------------------------------------------- |
| 11 | + |
| 12 | +from msrest.service_client import SDKClient |
| 13 | +from msrest import Serializer, Deserializer |
| 14 | + |
| 15 | +from ._configuration import AnomalyDetectorClientConfiguration |
| 16 | +from .operations import AnomalyDetectorClientOperationsMixin |
| 17 | +from . import models |
| 18 | + |
| 19 | + |
| 20 | +class AnomalyDetectorClient(AnomalyDetectorClientOperationsMixin, SDKClient): |
| 21 | + """The Anomaly Detector API detects anomalies automatically in time series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a time range without preparing time series in client side. Besides the above three functionalities, stateful model also provide group based detection and labeling service. By leveraging labeling service user can provide labels for each detection result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is a kind of group based detection, this detection will find inconsistency ones in a set of time series. By using anomaly detector service, business customers can discover incidents and establish a logic flow for root cause analysis. |
| 22 | +
|
| 23 | + :ivar config: Configuration for client. |
| 24 | + :vartype config: AnomalyDetectorClientConfiguration |
| 25 | +
|
| 26 | + :param endpoint: Supported Cognitive Services endpoints (protocol and |
| 27 | + hostname, for example: https://westus2.api.cognitive.microsoft.com). |
| 28 | + :type endpoint: str |
| 29 | + :param credentials: Subscription credentials which uniquely identify |
| 30 | + client subscription. |
| 31 | + :type credentials: None |
| 32 | + """ |
| 33 | + |
| 34 | + def __init__( |
| 35 | + self, endpoint, credentials): |
| 36 | + |
| 37 | + self.config = AnomalyDetectorClientConfiguration(endpoint, credentials) |
| 38 | + super(AnomalyDetectorClient, self).__init__(self.config.credentials, self.config) |
| 39 | + |
| 40 | + client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} |
| 41 | + self.api_version = '1.0' |
| 42 | + self._serialize = Serializer(client_models) |
| 43 | + self._deserialize = Deserializer(client_models) |
| 44 | + |
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