# 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
from msrest.exceptions import HttpOperationError
[docs]class APIError(Model):
"""Error information returned by the API.
:param code: The error code.
:type code: object
:param message: A message explaining the error reported by the service.
:type message: str
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'object'},
'message': {'key': 'message', 'type': 'str'},
}
def __init__(self, *, code=None, message: str=None, **kwargs) -> None:
super(APIError, self).__init__(**kwargs)
self.code = code
self.message = message
[docs]class APIErrorException(HttpOperationError):
"""Server responsed with exception of type: 'APIError'.
:param deserialize: A deserializer
:param response: Server response to be deserialized.
"""
def __init__(self, deserialize, response, *args):
super(APIErrorException, self).__init__(deserialize, response, 'APIError', *args)
[docs]class ChangePointDetectRequest(Model):
"""ChangePointDetectRequest.
All required parameters must be populated in order to send to Azure.
:param series: Required. Time series data points. Points should be sorted
by timestamp in ascending order to match the change point detection
result.
:type series: list[~azure.cognitiveservices.anomalydetector.models.Point]
:param granularity: Required. Can only be one of yearly, monthly, weekly,
daily, hourly, minutely or secondly. Granularity is used for verify
whether input series is valid. Possible values include: 'yearly',
'monthly', 'weekly', 'daily', 'hourly', 'minutely', 'secondly'
:type granularity: str or
~azure.cognitiveservices.anomalydetector.models.Granularity
:param custom_interval: Custom Interval is used to set non-standard time
interval, for example, if the series is 5 minutes, request can be set as
{"granularity":"minutely", "customInterval":5}.
:type custom_interval: int
:param period: Optional argument, periodic value of a time series. If the
value is null or does not present, the API will determine the period
automatically.
:type period: int
:param stable_trend_window: Optional argument, advanced model parameter, a
default stableTrendWindow will be used in detection.
:type stable_trend_window: int
:param threshold: Optional argument, advanced model parameter, between
0.0-1.0, the lower the value is, the larger the trend error will be which
means less change point will be accepted.
:type threshold: float
"""
_validation = {
'series': {'required': True},
'granularity': {'required': True},
}
_attribute_map = {
'series': {'key': 'series', 'type': '[Point]'},
'granularity': {'key': 'granularity', 'type': 'Granularity'},
'custom_interval': {'key': 'customInterval', 'type': 'int'},
'period': {'key': 'period', 'type': 'int'},
'stable_trend_window': {'key': 'stableTrendWindow', 'type': 'int'},
'threshold': {'key': 'threshold', 'type': 'float'},
}
def __init__(self, *, series, granularity, custom_interval: int=None, period: int=None, stable_trend_window: int=None, threshold: float=None, **kwargs) -> None:
super(ChangePointDetectRequest, self).__init__(**kwargs)
self.series = series
self.granularity = granularity
self.custom_interval = custom_interval
self.period = period
self.stable_trend_window = stable_trend_window
self.threshold = threshold
[docs]class ChangePointDetectResponse(Model):
"""ChangePointDetectResponse.
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 is_change_point: Required. isChangePoint contains change point
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_change_point: list[bool]
:param confidence_scores: Required. the change point confidence of each
point
:type confidence_scores: list[float]
"""
_validation = {
'period': {'required': True},
'is_change_point': {'required': True},
'confidence_scores': {'required': True},
}
_attribute_map = {
'period': {'key': 'period', 'type': 'int'},
'is_change_point': {'key': 'isChangePoint', 'type': '[bool]'},
'confidence_scores': {'key': 'confidenceScores', 'type': '[float]'},
}
def __init__(self, *, period: int, is_change_point, confidence_scores, **kwargs) -> None:
super(ChangePointDetectResponse, self).__init__(**kwargs)
self.period = period
self.is_change_point = is_change_point
self.confidence_scores = confidence_scores
[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
[docs]class LastDetectResponse(Model):
"""LastDetectResponse.
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 suggested_window: Required. Suggested input series points needed
for detecting the latest point.
:type suggested_window: int
:param expected_value: Required. Expected value of the latest point.
:type expected_value: float
:param upper_margin: Required. Upper margin of the latest point.
UpperMargin is used to calculate upperBoundary, which equals to
expectedValue + (100 - marginScale)*upperMargin. If the value of latest
point is between upperBoundary and lowerBoundary, it should be treated as
normal value. By adjusting marginScale value, anomaly status of latest
point can be changed.
:type upper_margin: float
:param lower_margin: Required. Lower margin of the latest point.
LowerMargin is used to calculate lowerBoundary, which equals to
expectedValue - (100 - marginScale)*lowerMargin.
:type lower_margin: float
:param is_anomaly: Required. Anomaly status of the latest point, true
means the latest point is an anomaly either in negative direction or
positive direction.
:type is_anomaly: bool
:param is_negative_anomaly: Required. Anomaly status in negative direction
of the latest point. True means the latest point is an anomaly and its
real value is smaller than the expected one.
:type is_negative_anomaly: bool
:param is_positive_anomaly: Required. Anomaly status in positive direction
of the latest point. True means the latest point is an anomaly and its
real value is larger than the expected one.
:type is_positive_anomaly: bool
"""
_validation = {
'period': {'required': True},
'suggested_window': {'required': True},
'expected_value': {'required': True},
'upper_margin': {'required': True},
'lower_margin': {'required': True},
'is_anomaly': {'required': True},
'is_negative_anomaly': {'required': True},
'is_positive_anomaly': {'required': True},
}
_attribute_map = {
'period': {'key': 'period', 'type': 'int'},
'suggested_window': {'key': 'suggestedWindow', 'type': 'int'},
'expected_value': {'key': 'expectedValue', 'type': 'float'},
'upper_margin': {'key': 'upperMargin', 'type': 'float'},
'lower_margin': {'key': 'lowerMargin', '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, suggested_window: int, expected_value: float, upper_margin: float, lower_margin: float, is_anomaly: bool, is_negative_anomaly: bool, is_positive_anomaly: bool, **kwargs) -> None:
super(LastDetectResponse, self).__init__(**kwargs)
self.period = period
self.suggested_window = suggested_window
self.expected_value = expected_value
self.upper_margin = upper_margin
self.lower_margin = lower_margin
self.is_anomaly = is_anomaly
self.is_negative_anomaly = is_negative_anomaly
self.is_positive_anomaly = is_positive_anomaly
[docs]class Point(Model):
"""Point.
All required parameters must be populated in order to send to Azure.
:param timestamp: Required. Timestamp of a data point (ISO8601 format).
:type timestamp: datetime
:param value: Required. The measurement of that point, should be float.
:type value: float
"""
_validation = {
'timestamp': {'required': True},
'value': {'required': True},
}
_attribute_map = {
'timestamp': {'key': 'timestamp', 'type': 'iso-8601'},
'value': {'key': 'value', 'type': 'float'},
}
def __init__(self, *, timestamp, value: float, **kwargs) -> None:
super(Point, self).__init__(**kwargs)
self.timestamp = timestamp
self.value = value
[docs]class Request(Model):
"""Request.
All required parameters must be populated in order to send to Azure.
:param series: Required. Time series data points. Points should be sorted
by timestamp in ascending order to match the anomaly detection result. If
the data is not sorted correctly or there is duplicated timestamp, the API
will not work. In such case, an error message will be returned.
:type series: list[~azure.cognitiveservices.anomalydetector.models.Point]
:param granularity: Required. Possible values include: 'yearly',
'monthly', 'weekly', 'daily', 'hourly', 'minutely', 'secondly'
:type granularity: str or
~azure.cognitiveservices.anomalydetector.models.Granularity
:param custom_interval: Custom Interval is used to set non-standard time
interval, for example, if the series is 5 minutes, request can be set as
{"granularity":"minutely", "customInterval":5}.
:type custom_interval: int
:param period: Optional argument, periodic value of a time series. If the
value is null or does not present, the API will determine the period
automatically.
:type period: int
:param max_anomaly_ratio: Optional argument, advanced model parameter, max
anomaly ratio in a time series.
:type max_anomaly_ratio: float
:param sensitivity: Optional argument, advanced model parameter, between
0-99, the lower the value is, the larger the margin value will be which
means less anomalies will be accepted.
:type sensitivity: int
"""
_validation = {
'series': {'required': True},
'granularity': {'required': True},
}
_attribute_map = {
'series': {'key': 'series', 'type': '[Point]'},
'granularity': {'key': 'granularity', 'type': 'Granularity'},
'custom_interval': {'key': 'customInterval', 'type': 'int'},
'period': {'key': 'period', 'type': 'int'},
'max_anomaly_ratio': {'key': 'maxAnomalyRatio', 'type': 'float'},
'sensitivity': {'key': 'sensitivity', 'type': 'int'},
}
def __init__(self, *, series, granularity, custom_interval: int=None, period: int=None, max_anomaly_ratio: float=None, sensitivity: int=None, **kwargs) -> None:
super(Request, self).__init__(**kwargs)
self.series = series
self.granularity = granularity
self.custom_interval = custom_interval
self.period = period
self.max_anomaly_ratio = max_anomaly_ratio
self.sensitivity = sensitivity