# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import TYPE_CHECKING
from azure.core import PipelineClient
from msrest import Deserializer, Serializer
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any
from azure.core.credentials import AzureKeyCredential
from azure.core.pipeline.transport import HttpRequest, HttpResponse
from ._configuration import AnomalyDetectorClientConfiguration
from .operations import AnomalyDetectorClientOperationsMixin
from . import models
[docs]class AnomalyDetectorClient(AnomalyDetectorClientOperationsMixin):
"""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.
:param credential: Credential needed for the client to connect to Azure.
:type credential: ~azure.core.credentials.AzureKeyCredential
:param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: https://westus2.api.cognitive.microsoft.com).
:type endpoint: str
"""
def __init__(
self,
credential, # type: AzureKeyCredential
endpoint, # type: str
**kwargs # type: Any
):
# type: (...) -> None
base_url = '{Endpoint}/anomalydetector/v1.1-preview'
self._config = AnomalyDetectorClientConfiguration(credential, endpoint, **kwargs)
self._client = PipelineClient(base_url=base_url, config=self._config, **kwargs)
client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}
self._serialize = Serializer(client_models)
self._serialize.client_side_validation = False
self._deserialize = Deserializer(client_models)
def _send_request(self, http_request, **kwargs):
# type: (HttpRequest, Any) -> HttpResponse
"""Runs the network request through the client's chained policies.
:param http_request: The network request you want to make. Required.
:type http_request: ~azure.core.pipeline.transport.HttpRequest
:keyword bool stream: Whether the response payload will be streamed. Defaults to True.
:return: The response of your network call. Does not do error handling on your response.
:rtype: ~azure.core.pipeline.transport.HttpResponse
"""
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
http_request.url = self._client.format_url(http_request.url, **path_format_arguments)
stream = kwargs.pop("stream", True)
pipeline_response = self._client._pipeline.run(http_request, stream=stream, **kwargs)
return pipeline_response.http_response
[docs] def close(self):
# type: () -> None
self._client.close()
def __enter__(self):
# type: () -> AnomalyDetectorClient
self._client.__enter__()
return self
def __exit__(self, *exc_details):
# type: (Any) -> None
self._client.__exit__(*exc_details)