Source code for azure.cognitiveservices.anomalydetector.anomaly_detector_client

# 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 msrest.service_client import SDKClient
from msrest import Configuration, Serializer, Deserializer
from .version import VERSION
from msrest.pipeline import ClientRawResponse
from . import models


class AnomalyDetectorClientConfiguration(Configuration):
    """Configuration for AnomalyDetectorClient
    Note that all parameters used to create this instance are saved as instance
    attributes.

    :param endpoint: Supported Cognitive Services endpoints (protocol and
     hostname, for example: https://westus2.api.cognitive.microsoft.com).
    :type endpoint: str
    :param credentials: Subscription credentials which uniquely identify
     client subscription.
    :type credentials: None
    """

    def __init__(
            self, endpoint, credentials):

        if endpoint is None:
            raise ValueError("Parameter 'endpoint' must not be None.")
        if credentials is None:
            raise ValueError("Parameter 'credentials' must not be None.")
        base_url = '{Endpoint}/anomalydetector/v1.0'

        super(AnomalyDetectorClientConfiguration, self).__init__(base_url)

        self.add_user_agent('azure-cognitiveservices-anomalydetector/{}'.format(VERSION))

        self.endpoint = endpoint
        self.credentials = credentials


[docs]class AnomalyDetectorClient(SDKClient): """The Anomaly Detector API detects anomalies automatically in time series data. It supports two functionalities, one is for detecting the whole series with model trained by the timeseries, another is detecting last point with model trained by points before. By using this service, business customers can discover incidents and establish a logic flow for root cause analysis. :ivar config: Configuration for client. :vartype config: AnomalyDetectorClientConfiguration :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: https://westus2.api.cognitive.microsoft.com). :type endpoint: str :param credentials: Subscription credentials which uniquely identify client subscription. :type credentials: None """ def __init__( self, endpoint, credentials): self.config = AnomalyDetectorClientConfiguration(endpoint, credentials) super(AnomalyDetectorClient, self).__init__(self.config.credentials, self.config) client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} self.api_version = '1.0' self._serialize = Serializer(client_models) self._deserialize = Deserializer(client_models)
[docs] def entire_detect( self, body, custom_headers=None, raw=False, **operation_config): """Detect anomalies for the entire series in batch. This operation generates a model using 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.cognitiveservices.anomalydetector.models.Request :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: EntireDetectResponse or ClientRawResponse if raw=true :rtype: ~azure.cognitiveservices.anomalydetector.models.EntireDetectResponse or ~msrest.pipeline.ClientRawResponse :raises: :class:`APIErrorException<azure.cognitiveservices.anomalydetector.models.APIErrorException>` """ # Construct URL url = self.entire_detect.metadata['url'] 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 = {} # Construct headers header_parameters = {} header_parameters['Accept'] = 'application/json' header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct body body_content = self._serialize.body(body, 'Request') # Construct and send request request = self._client.post(url, query_parameters, header_parameters, body_content) response = self._client.send(request, stream=False, **operation_config) if response.status_code not in [200]: raise models.APIErrorException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('EntireDetectResponse', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized
entire_detect.metadata = {'url': '/timeseries/entire/detect'}
[docs] def last_detect( self, body, custom_headers=None, raw=False, **operation_config): """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.cognitiveservices.anomalydetector.models.Request :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: LastDetectResponse or ClientRawResponse if raw=true :rtype: ~azure.cognitiveservices.anomalydetector.models.LastDetectResponse or ~msrest.pipeline.ClientRawResponse :raises: :class:`APIErrorException<azure.cognitiveservices.anomalydetector.models.APIErrorException>` """ # Construct URL url = self.last_detect.metadata['url'] 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 = {} # Construct headers header_parameters = {} header_parameters['Accept'] = 'application/json' header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct body body_content = self._serialize.body(body, 'Request') # Construct and send request request = self._client.post(url, query_parameters, header_parameters, body_content) response = self._client.send(request, stream=False, **operation_config) if response.status_code not in [200]: raise models.APIErrorException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('LastDetectResponse', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized
last_detect.metadata = {'url': '/timeseries/last/detect'}