Source code for azure.cognitiveservices.anomalydetector.models.entire_detect_response_py3

# 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.serialization import Model


[docs]class EntireDetectResponse(Model): """EntireDetectResponse. All required parameters must be populated in order to send to Azure. :param period: Required. Frequency extracted from the series, zero means no recurrent pattern has been found. :type period: int :param expected_values: Required. ExpectedValues contain expected value for each input point. The index of the array is consistent with the input series. :type expected_values: list[float] :param upper_margins: Required. UpperMargins contain upper margin of each input point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. Anomalies in response can be filtered by upperBoundary and lowerBoundary. By adjusting marginScale value, less significant anomalies can be filtered in client side. The index of the array is consistent with the input series. :type upper_margins: list[float] :param lower_margins: Required. LowerMargins contain lower margin of each input point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin. Points between the boundary can be marked as normal ones in client side. The index of the array is consistent with the input series. :type lower_margins: list[float] :param is_anomaly: Required. IsAnomaly contains anomaly properties for each input point. True means an anomaly either negative or positive has been detected. The index of the array is consistent with the input series. :type is_anomaly: list[bool] :param is_negative_anomaly: Required. IsNegativeAnomaly contains anomaly status in negative direction for each input point. True means a negative anomaly has been detected. A negative anomaly means the point is detected as an anomaly and its real value is smaller than the expected one. The index of the array is consistent with the input series. :type is_negative_anomaly: list[bool] :param is_positive_anomaly: Required. IsPositiveAnomaly contain anomaly status in positive direction for each input point. True means a positive anomaly has been detected. A positive anomaly means the point is detected as an anomaly and its real value is larger than the expected one. The index of the array is consistent with the input series. :type is_positive_anomaly: list[bool] """ _validation = { 'period': {'required': True}, 'expected_values': {'required': True}, 'upper_margins': {'required': True}, 'lower_margins': {'required': True}, 'is_anomaly': {'required': True}, 'is_negative_anomaly': {'required': True}, 'is_positive_anomaly': {'required': True}, } _attribute_map = { 'period': {'key': 'period', 'type': 'int'}, 'expected_values': {'key': 'expectedValues', 'type': '[float]'}, 'upper_margins': {'key': 'upperMargins', 'type': '[float]'}, 'lower_margins': {'key': 'lowerMargins', 'type': '[float]'}, 'is_anomaly': {'key': 'isAnomaly', 'type': '[bool]'}, 'is_negative_anomaly': {'key': 'isNegativeAnomaly', 'type': '[bool]'}, 'is_positive_anomaly': {'key': 'isPositiveAnomaly', 'type': '[bool]'}, } def __init__(self, *, period: int, expected_values, upper_margins, lower_margins, is_anomaly, is_negative_anomaly, is_positive_anomaly, **kwargs) -> None: super(EntireDetectResponse, self).__init__(**kwargs) self.period = period self.expected_values = expected_values self.upper_margins = upper_margins self.lower_margins = lower_margins self.is_anomaly = is_anomaly self.is_negative_anomaly = is_negative_anomaly self.is_positive_anomaly = is_positive_anomaly