nannyml.io.db.entities module

Contains the definitions of the database entities that map directly to the underlying table definitions.

Every Result class has a matching Entity class, which implies that each calculator or estimator will export its results into a specific table.

class nannyml.io.db.entities.CBPEPerformanceMetric(*, id: Optional[int] = None, model_id: Optional[int] = None, run_id: int = None, start_timestamp: datetime, end_timestamp: datetime, timestamp: datetime, metric_name: str, value: float, alert: bool, upper_threshold: Optional[float], lower_threshold: Optional[float])

Bases: Metric

Represents results of the CBPE estimator.

Stored in the cbpe_performance_metrics table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

alert: bool

Indicates if the method raised an alert for this Chunk

end_timestamp: datetime

The end datetime of the Chunk

id: Optional[int]

The technical identifier for this database row

lower_threshold: Optional[float]

The lower alerting threshold value

metric_name: str

The name of the method being calculated, e.g. jensen_shannon or chi2

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'from_attributes': True, 'protected_namespaces': (), 'read_from_attributes': True, 'read_with_orm_mode': True, 'registry': PydanticUndefined, 'table': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'alert': FieldInfo(annotation=bool, required=True), 'end_timestamp': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'lower_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'metric_name': FieldInfo(annotation=str, required=True), 'model_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'run_id': FieldInfo(annotation=int, required=False, default=None), 'start_timestamp': FieldInfo(annotation=datetime, required=True), 'timestamp': FieldInfo(annotation=datetime, required=True), 'upper_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'value': FieldInfo(annotation=float, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_id: Optional[int]

Foreign key pointing to a record in the model table

run_id: int

Foreign key pointing to a record in the run table

start_timestamp: datetime

The start datetime of the Chunk

timestamp: datetime

The ‘’center’’ timestamp of the Chunk, i.e. the mean of the start and end timestamps

upper_threshold: Optional[float]

The upper alerting threshold value

value: float

The value returned by the method

class nannyml.io.db.entities.DLEPerformanceMetric(*, id: Optional[int] = None, model_id: Optional[int] = None, run_id: int = None, start_timestamp: datetime, end_timestamp: datetime, timestamp: datetime, metric_name: str, value: float, alert: bool, upper_threshold: Optional[float], lower_threshold: Optional[float])

Bases: Metric

Represents results of the DLE estimator.

Stored in the dle_performance_metrics table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

alert: bool

Indicates if the method raised an alert for this Chunk

end_timestamp: datetime

The end datetime of the Chunk

id: Optional[int]

The technical identifier for this database row

lower_threshold: Optional[float]

The lower alerting threshold value

metric_name: str

The name of the method being calculated, e.g. jensen_shannon or chi2

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'from_attributes': True, 'protected_namespaces': (), 'read_from_attributes': True, 'read_with_orm_mode': True, 'registry': PydanticUndefined, 'table': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'alert': FieldInfo(annotation=bool, required=True), 'end_timestamp': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'lower_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'metric_name': FieldInfo(annotation=str, required=True), 'model_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'run_id': FieldInfo(annotation=int, required=False, default=None), 'start_timestamp': FieldInfo(annotation=datetime, required=True), 'timestamp': FieldInfo(annotation=datetime, required=True), 'upper_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'value': FieldInfo(annotation=float, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_id: Optional[int]

Foreign key pointing to a record in the model table

run_id: int

Foreign key pointing to a record in the run table

start_timestamp: datetime

The start datetime of the Chunk

timestamp: datetime

The ‘’center’’ timestamp of the Chunk, i.e. the mean of the start and end timestamps

upper_threshold: Optional[float]

The upper alerting threshold value

value: float

The value returned by the method

class nannyml.io.db.entities.DataReconstructionFeatureDriftMetric(*, id: Optional[int] = None, model_id: Optional[int] = None, run_id: int = None, start_timestamp: datetime, end_timestamp: datetime, timestamp: datetime, metric_name: str, value: float, alert: bool, upper_threshold: Optional[float], lower_threshold: Optional[float])

Bases: Metric

DataReconstructionDriftCalculator results.

Stored in the data_reconstruction_feature_drift_metrics table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

alert: bool

Indicates if the method raised an alert for this Chunk

end_timestamp: datetime

The end datetime of the Chunk

id: Optional[int]

The technical identifier for this database row

lower_threshold: Optional[float]

The lower alerting threshold value

metric_name: str

The name of the method being calculated, e.g. jensen_shannon or chi2

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'from_attributes': True, 'protected_namespaces': (), 'read_from_attributes': True, 'read_with_orm_mode': True, 'registry': PydanticUndefined, 'table': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'alert': FieldInfo(annotation=bool, required=True), 'end_timestamp': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'lower_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'metric_name': FieldInfo(annotation=str, required=True), 'model_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'run_id': FieldInfo(annotation=int, required=False, default=None), 'start_timestamp': FieldInfo(annotation=datetime, required=True), 'timestamp': FieldInfo(annotation=datetime, required=True), 'upper_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'value': FieldInfo(annotation=float, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_id: Optional[int]

Foreign key pointing to a record in the model table

run_id: int

Foreign key pointing to a record in the run table

start_timestamp: datetime

The start datetime of the Chunk

timestamp: datetime

The ‘’center’’ timestamp of the Chunk, i.e. the mean of the start and end timestamps

upper_threshold: Optional[float]

The upper alerting threshold value

value: float

The value returned by the method

class nannyml.io.db.entities.Metric(*, id: Optional[int] = None, model_id: Optional[int] = None, run_id: int = None, start_timestamp: datetime, end_timestamp: datetime, timestamp: datetime, metric_name: str, value: float, alert: bool)

Bases: SQLModel

Base Metric definition.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

alert: bool

Indicates if the method raised an alert for this Chunk

end_timestamp: datetime

The end datetime of the Chunk

id: Optional[int]

The technical identifier for this database row

metric_name: str

The name of the method being calculated, e.g. jensen_shannon or chi2

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'from_attributes': True, 'protected_namespaces': (), 'registry': PydanticUndefined}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'alert': FieldInfo(annotation=bool, required=True), 'end_timestamp': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'metric_name': FieldInfo(annotation=str, required=True), 'model_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'run_id': FieldInfo(annotation=int, required=False, default=None), 'start_timestamp': FieldInfo(annotation=datetime, required=True), 'timestamp': FieldInfo(annotation=datetime, required=True), 'value': FieldInfo(annotation=float, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_id: Optional[int]

Foreign key pointing to a record in the model table

run_id: int

Foreign key pointing to a record in the run table

start_timestamp: datetime

The start datetime of the Chunk

timestamp: datetime

The ‘’center’’ timestamp of the Chunk, i.e. the mean of the start and end timestamps

value: float

The value returned by the method

class nannyml.io.db.entities.MissingValuesMetric(*, id: Optional[int] = None, model_id: Optional[int] = None, run_id: int = None, start_timestamp: datetime, end_timestamp: datetime, timestamp: datetime, metric_name: str, value: float, alert: bool, column_name: str, upper_threshold: Optional[float], lower_threshold: Optional[float])

Bases: Metric

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

alert: bool

Indicates if the method raised an alert for this Chunk

column_name: str

The name of the column this metric belongs to

end_timestamp: datetime

The end datetime of the Chunk

id: Optional[int]

The technical identifier for this database row

lower_threshold: Optional[float]

The lower alerting threshold value

metric_name: str

The name of the method being calculated, e.g. jensen_shannon or chi2

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'from_attributes': True, 'protected_namespaces': (), 'read_from_attributes': True, 'read_with_orm_mode': True, 'registry': PydanticUndefined, 'table': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'alert': FieldInfo(annotation=bool, required=True), 'column_name': FieldInfo(annotation=str, required=True), 'end_timestamp': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'lower_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'metric_name': FieldInfo(annotation=str, required=True), 'model_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'run_id': FieldInfo(annotation=int, required=False, default=None), 'start_timestamp': FieldInfo(annotation=datetime, required=True), 'timestamp': FieldInfo(annotation=datetime, required=True), 'upper_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'value': FieldInfo(annotation=float, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_id: Optional[int]

Foreign key pointing to a record in the model table

run_id: int

Foreign key pointing to a record in the run table

start_timestamp: datetime

The start datetime of the Chunk

timestamp: datetime

The ‘’center’’ timestamp of the Chunk, i.e. the mean of the start and end timestamps

upper_threshold: Optional[float]

The upper alerting threshold value

value: float

The value returned by the method

class nannyml.io.db.entities.Model(*, id: Optional[int] = None, name: str)

Bases: SQLModel

Represents a Model.

Only created when the model_name property of the DatabaseWriter was given. The id field here will act as a foreign key in the run table and all metric tables.

Stored in the model table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

id: Optional[int]

A technical key that is used as a foreign key in the other tables

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'from_attributes': True, 'read_from_attributes': True, 'read_with_orm_mode': True, 'registry': PydanticUndefined, 'table': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

name: str

Optional model name that might be useful in visualizations e.g. in Grafana dashboards

runs: Mapped[List[Run]]

List of NannyML runs

class nannyml.io.db.entities.RealizedPerformanceMetric(*, id: Optional[int] = None, model_id: Optional[int] = None, run_id: int = None, start_timestamp: datetime, end_timestamp: datetime, timestamp: datetime, metric_name: str, value: float, alert: bool, upper_threshold: Optional[float], lower_threshold: Optional[float])

Bases: Metric

Represents results of the PerformanceCalculator.

Stored in the realized_performance_metrics table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

alert: bool

Indicates if the method raised an alert for this Chunk

end_timestamp: datetime

The end datetime of the Chunk

id: Optional[int]

The technical identifier for this database row

lower_threshold: Optional[float]

The lower alerting threshold value

metric_name: str

The name of the method being calculated, e.g. jensen_shannon or chi2

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'from_attributes': True, 'protected_namespaces': (), 'read_from_attributes': True, 'read_with_orm_mode': True, 'registry': PydanticUndefined, 'table': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'alert': FieldInfo(annotation=bool, required=True), 'end_timestamp': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'lower_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'metric_name': FieldInfo(annotation=str, required=True), 'model_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'run_id': FieldInfo(annotation=int, required=False, default=None), 'start_timestamp': FieldInfo(annotation=datetime, required=True), 'timestamp': FieldInfo(annotation=datetime, required=True), 'upper_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'value': FieldInfo(annotation=float, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_id: Optional[int]

Foreign key pointing to a record in the model table

run_id: int

Foreign key pointing to a record in the run table

start_timestamp: datetime

The start datetime of the Chunk

timestamp: datetime

The ‘’center’’ timestamp of the Chunk, i.e. the mean of the start and end timestamps

upper_threshold: Optional[float]

The upper alerting threshold value

value: float

The value returned by the method

class nannyml.io.db.entities.Run(*, id: Optional[int] = None, model_id: Optional[int] = None, execution_timestamp: datetime = datetime.datetime(2024, 7, 19, 21, 20, 25, 688896))

Bases: SQLModel

Represents a NannyML run, allowing to filter results based on what run generated them.

The id field here will act as a foreign key in all metric tables.

Stored in the run table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

execution_timestamp: datetime

Execution time of NannyML run

id: Optional[int]

Foreign key in all metric tables

model: Mapped[Model]

The actual Model class instance that is linked to the run

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'from_attributes': True, 'protected_namespaces': (), 'read_from_attributes': True, 'read_with_orm_mode': True, 'registry': PydanticUndefined, 'table': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'execution_timestamp': FieldInfo(annotation=datetime, required=False, default=datetime.datetime(2024, 7, 19, 21, 20, 25, 688896)), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'model_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_id: Optional[int]

Used to link a run to a model

class nannyml.io.db.entities.UnivariateDriftMetric(*, id: Optional[int] = None, model_id: Optional[int] = None, run_id: int = None, start_timestamp: datetime, end_timestamp: datetime, timestamp: datetime, metric_name: str, value: float, alert: bool, column_name: str)

Bases: Metric

Represents results of the UnivariateDriftCalculator.

Stored in the univariate_drift_metrics table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

alert: bool

Indicates if the method raised an alert for this Chunk

column_name: str

The name of the column this metric belongs to

end_timestamp: datetime

The end datetime of the Chunk

id: Optional[int]

The technical identifier for this database row

metric_name: str

The name of the method being calculated, e.g. jensen_shannon or chi2

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'from_attributes': True, 'protected_namespaces': (), 'read_from_attributes': True, 'read_with_orm_mode': True, 'registry': PydanticUndefined, 'table': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'alert': FieldInfo(annotation=bool, required=True), 'column_name': FieldInfo(annotation=str, required=True), 'end_timestamp': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'metric_name': FieldInfo(annotation=str, required=True), 'model_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'run_id': FieldInfo(annotation=int, required=False, default=None), 'start_timestamp': FieldInfo(annotation=datetime, required=True), 'timestamp': FieldInfo(annotation=datetime, required=True), 'value': FieldInfo(annotation=float, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_id: Optional[int]

Foreign key pointing to a record in the model table

run_id: int

Foreign key pointing to a record in the run table

start_timestamp: datetime

The start datetime of the Chunk

timestamp: datetime

The ‘’center’’ timestamp of the Chunk, i.e. the mean of the start and end timestamps

value: float

The value returned by the method

class nannyml.io.db.entities.UnseenValuesMetric(*, id: Optional[int] = None, model_id: Optional[int] = None, run_id: int = None, start_timestamp: datetime, end_timestamp: datetime, timestamp: datetime, metric_name: str, value: float, alert: bool, column_name: str, upper_threshold: Optional[float], lower_threshold: Optional[float])

Bases: Metric

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

alert: bool

Indicates if the method raised an alert for this Chunk

column_name: str

The name of the column this metric belongs to

end_timestamp: datetime

The end datetime of the Chunk

id: Optional[int]

The technical identifier for this database row

lower_threshold: Optional[float]

The lower alerting threshold value

metric_name: str

The name of the method being calculated, e.g. jensen_shannon or chi2

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'from_attributes': True, 'protected_namespaces': (), 'read_from_attributes': True, 'read_with_orm_mode': True, 'registry': PydanticUndefined, 'table': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'alert': FieldInfo(annotation=bool, required=True), 'column_name': FieldInfo(annotation=str, required=True), 'end_timestamp': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'lower_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'metric_name': FieldInfo(annotation=str, required=True), 'model_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'run_id': FieldInfo(annotation=int, required=False, default=None), 'start_timestamp': FieldInfo(annotation=datetime, required=True), 'timestamp': FieldInfo(annotation=datetime, required=True), 'upper_threshold': FieldInfo(annotation=Union[float, NoneType], required=True), 'value': FieldInfo(annotation=float, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_id: Optional[int]

Foreign key pointing to a record in the model table

run_id: int

Foreign key pointing to a record in the run table

start_timestamp: datetime

The start datetime of the Chunk

timestamp: datetime

The ‘’center’’ timestamp of the Chunk, i.e. the mean of the start and end timestamps

upper_threshold: Optional[float]

The upper alerting threshold value

value: float

The value returned by the method