# 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
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.paging import ItemPaged
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpRequest, HttpResponse
from .. import models as _models
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, IO, Iterable, Optional, TypeVar
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
[docs]class AnomalyDetectorClientOperationsMixin(object):
[docs] def detect_entire_series(
self,
body, # type: "_models.DetectRequest"
**kwargs # type: Any
):
# type: (...) -> "_models.EntireDetectResponse"
"""Detect anomalies for the entire series in batch.
This operation generates a model with an entire series, each point is detected with the same
model. With this method, points before and after a certain point are used to determine whether
it is an anomaly. The entire detection can give user an overall status of the time series.
:param body: Time series points and period if needed. Advanced model parameters can also be set
in the request.
:type body: ~azure.ai.anomalydetector.models.DetectRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: EntireDetectResponse, or the result of cls(response)
:rtype: ~azure.ai.anomalydetector.models.EntireDetectResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.EntireDetectResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self.detect_entire_series.metadata['url'] # type: ignore
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(body, 'DetectRequest')
body_content_kwargs['content'] = body_content
request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.AnomalyDetectorError, response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('EntireDetectResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
detect_entire_series.metadata = {'url': '/timeseries/entire/detect'} # type: ignore
[docs] def detect_last_point(
self,
body, # type: "_models.DetectRequest"
**kwargs # type: Any
):
# type: (...) -> "_models.LastDetectResponse"
"""Detect anomaly status of the latest point in time series.
This operation generates a model using points before the latest one. With this method, only
historical points are used to determine whether the target point is an anomaly. The latest
point detecting operation matches the scenario of real-time monitoring of business metrics.
:param body: Time series points and period if needed. Advanced model parameters can also be set
in the request.
:type body: ~azure.ai.anomalydetector.models.DetectRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: LastDetectResponse, or the result of cls(response)
:rtype: ~azure.ai.anomalydetector.models.LastDetectResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.LastDetectResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self.detect_last_point.metadata['url'] # type: ignore
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(body, 'DetectRequest')
body_content_kwargs['content'] = body_content
request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.AnomalyDetectorError, response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('LastDetectResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
detect_last_point.metadata = {'url': '/timeseries/last/detect'} # type: ignore
[docs] def detect_change_point(
self,
body, # type: "_models.ChangePointDetectRequest"
**kwargs # type: Any
):
# type: (...) -> "_models.ChangePointDetectResponse"
"""Detect change point for the entire series.
Evaluate change point score of every series point.
:param body: Time series points and granularity is needed. Advanced model parameters can also
be set in the request if needed.
:type body: ~azure.ai.anomalydetector.models.ChangePointDetectRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ChangePointDetectResponse, or the result of cls(response)
:rtype: ~azure.ai.anomalydetector.models.ChangePointDetectResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ChangePointDetectResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self.detect_change_point.metadata['url'] # type: ignore
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(body, 'ChangePointDetectRequest')
body_content_kwargs['content'] = body_content
request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.AnomalyDetectorError, response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ChangePointDetectResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
detect_change_point.metadata = {'url': '/timeseries/changepoint/detect'} # type: ignore
[docs] def train_multivariate_model(
self,
model_request, # type: "_models.ModelInfo"
**kwargs # type: Any
):
# type: (...) -> None
"""Train a Multivariate Anomaly Detection Model.
Create and train a multivariate anomaly detection model. The request must include a source
parameter to indicate an externally accessible Azure storage Uri (preferably a Shared Access
Signature Uri). All time-series used in generate the model must be zipped into one single file.
Each time-series will be in a single CSV file in which the first column is timestamp and the
second column is value.
:param model_request: Training request.
:type model_request: ~azure.ai.anomalydetector.models.ModelInfo
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self.train_multivariate_model.metadata['url'] # type: ignore
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(model_request, 'ModelInfo')
body_content_kwargs['content'] = body_content
request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [201]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response)
raise HttpResponseError(response=response, model=error)
response_headers = {}
response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
if cls:
return cls(pipeline_response, None, response_headers)
train_multivariate_model.metadata = {'url': '/multivariate/models'} # type: ignore
[docs] def get_multivariate_model(
self,
model_id, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.Model"
"""Get Multivariate Model.
Get detailed information of multivariate model, including the training status and variables
used in the model.
:param model_id: Model identifier.
:type model_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: Model, or the result of cls(response)
:rtype: ~azure.ai.anomalydetector.models.Model
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.Model"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
accept = "application/json"
# Construct URL
url = self.get_multivariate_model.metadata['url'] # type: ignore
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
'modelId': self._serialize.url("model_id", model_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('Model', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_multivariate_model.metadata = {'url': '/multivariate/models/{modelId}'} # type: ignore
[docs] def delete_multivariate_model(
self,
model_id, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Delete Multivariate Model.
Delete an existing multivariate model according to the modelId.
:param model_id: Model identifier.
:type model_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
accept = "application/json"
# Construct URL
url = self.delete_multivariate_model.metadata['url'] # type: ignore
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
'modelId': self._serialize.url("model_id", model_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.delete(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response)
raise HttpResponseError(response=response, model=error)
if cls:
return cls(pipeline_response, None, {})
delete_multivariate_model.metadata = {'url': '/multivariate/models/{modelId}'} # type: ignore
[docs] def detect_anomaly(
self,
model_id, # type: str
detection_request, # type: "_models.DetectionRequest"
**kwargs # type: Any
):
# type: (...) -> None
"""Detect Multivariate Anomaly.
Submit detection multivariate anomaly task with the trained model of modelId, the input schema
should be the same with the training request. Thus request will be complete asynchronously and
will return a resultId for querying the detection result.The request should be a source link to
indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri).
All time-series used in generate the model must be zipped into one single file. Each
time-series will be as follows: the first column is timestamp and the second column is value.
:param model_id: Model identifier.
:type model_id: str
:param detection_request: Detect anomaly request.
:type detection_request: ~azure.ai.anomalydetector.models.DetectionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self.detect_anomaly.metadata['url'] # type: ignore
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
'modelId': self._serialize.url("model_id", model_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(detection_request, 'DetectionRequest')
body_content_kwargs['content'] = body_content
request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [201]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response)
raise HttpResponseError(response=response, model=error)
response_headers = {}
response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
if cls:
return cls(pipeline_response, None, response_headers)
detect_anomaly.metadata = {'url': '/multivariate/models/{modelId}/detect'} # type: ignore
[docs] def get_detection_result(
self,
result_id, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.DetectionResult"
"""Get Multivariate Anomaly Detection Result.
Get multivariate anomaly detection result based on resultId returned by the DetectAnomalyAsync
api.
:param result_id: Result identifier.
:type result_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: DetectionResult, or the result of cls(response)
:rtype: ~azure.ai.anomalydetector.models.DetectionResult
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.DetectionResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
accept = "application/json"
# Construct URL
url = self.get_detection_result.metadata['url'] # type: ignore
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
'resultId': self._serialize.url("result_id", result_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('DetectionResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_detection_result.metadata = {'url': '/multivariate/results/{resultId}'} # type: ignore
[docs] def export_model(
self,
model_id, # type: str
**kwargs # type: Any
):
# type: (...) -> IO
"""Export Multivariate Anomaly Detection Model as Zip file.
Export multivariate anomaly detection model based on modelId.
:param model_id: Model identifier.
:type model_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: IO, or the result of cls(response)
:rtype: IO
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[IO]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
accept = "application/zip"
# Construct URL
url = self.export_model.metadata['url'] # type: ignore
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
'modelId': self._serialize.url("model_id", model_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=True, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
response_headers = {}
response_headers['content-type']=self._deserialize('str', response.headers.get('content-type'))
deserialized = response.stream_download(self._client._pipeline)
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
export_model.metadata = {'url': '/multivariate/models/{modelId}/export'} # type: ignore
[docs] def list_multivariate_model(
self,
skip=0, # type: Optional[int]
top=5, # type: Optional[int]
**kwargs # type: Any
):
# type: (...) -> Iterable["_models.ModelList"]
"""List Multivariate Models.
List models of a subscription.
:param skip: $skip indicates how many models will be skipped.
:type skip: int
:param top: $top indicates how many models will be fetched.
:type top: int
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either ModelList or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.ai.anomalydetector.models.ModelList]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ModelList"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
accept = "application/json"
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list_multivariate_model.metadata['url'] # type: ignore
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
if skip is not None:
query_parameters['$skip'] = self._serialize.query("skip", skip, 'int')
if top is not None:
query_parameters['$top'] = self._serialize.query("top", top, 'int')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {} # type: Dict[str, Any]
path_format_arguments = {
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
request = self._client.get(url, query_parameters, header_parameters)
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('ModelList', pipeline_response)
list_of_elem = deserialized.models
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, iter(list_of_elem)
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response)
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, model=error)
return pipeline_response
return ItemPaged(
get_next, extract_data
)
list_multivariate_model.metadata = {'url': '/multivariate/models'} # type: ignore