Drift

Bin

Source code in python/scouter/drift/_drift.pyi

class Bin:
    @property
    def id(self) -> int:
        """Return the bin id."""

    @property
    def lower_limit(self) -> float:
        """Return the lower limit of the bin."""

    @property
    def upper_limit(self) -> Optional[float]:
        """Return the upper limit of the bin."""

    @property
    def proportion(self) -> float:
        """Return the proportion of data found in the bin."""

id property

Return the bin id.

lower_limit property

Return the lower limit of the bin.

proportion property

Return the proportion of data found in the bin.

upper_limit property

Return the upper limit of the bin.

CustomDriftProfile

Source code in python/scouter/drift/_drift.pyi

class CustomDriftProfile:
    def __init__(
        self,
        config: CustomMetricDriftConfig,
        metrics: list[CustomMetric],
    ):
        """Initialize a CustomDriftProfile instance.

        Args:
            config (CustomMetricDriftConfig):
                The configuration for custom metric drift detection.
            metrics (list[CustomMetric]):
                A list of CustomMetric objects representing the metrics to be monitored.

        Example:
            config = CustomMetricDriftConfig(...)
            metrics = [CustomMetric("accuracy", 0.95), CustomMetric("f1_score", 0.88)]
            profile = CustomDriftProfile(config, metrics, "1.0.0")
        """

    @property
    def config(self) -> CustomMetricDriftConfig:
        """Return the drift config"""

    @property
    def metrics(self) -> dict[str, float]:
        """Return custom metrics and their corresponding values"""

    @property
    def scouter_version(self) -> str:
        """Return scouter version used to create DriftProfile"""

    @property
    def custom_metrics(self) -> list[CustomMetric]:
        """Return custom metric objects that were used to create the drift profile"""

    def __str__(self) -> str:
        """Sting representation of DriftProfile"""

    def model_dump_json(self) -> str:
        """Return json representation of drift profile"""

    def model_dump(self) -> Dict[str, Any]:
        """Return dictionary representation of drift profile"""

    def save_to_json(self, path: Optional[Path] = None) -> Path:
        """Save drift profile to json file

        Args:
            path:
                Optional path to save the drift profile. If None, outputs to `custom_drift_profile.json`

        Returns:
            Path to the saved json file
        """

    @staticmethod
    def model_validate_json(json_string: str) -> "CustomDriftProfile":
        """Load drift profile from json

        Args:
            json_string:
                JSON string representation of the drift profile

        """

    @staticmethod
    def model_validate(data: Dict[str, Any]) -> "CustomDriftProfile":
        """Load drift profile from dictionary

        Args:
            data:
                DriftProfile dictionary
        """

    @staticmethod
    def from_file(path: Path) -> "CustomDriftProfile":
        """Load drift profile from file

        Args:
            path: Path to the file
        """

    def update_config_args(
        self,
        space: Optional[str] = None,
        name: Optional[str] = None,
        version: Optional[str] = None,
        alert_config: Optional[CustomMetricAlertConfig] = None,
    ) -> None:
        """Inplace operation that updates config args

        Args:
            space (Optional[str]):
                Model space
            name (Optional[str]):
                Model name
            version (Optional[str]):
                Model version
            alert_config (Optional[CustomMetricAlertConfig]):
                Custom metric alert configuration

        Returns:
            None
        """

config property

Return the drift config

custom_metrics property

Return custom metric objects that were used to create the drift profile

metrics property

Return custom metrics and their corresponding values

scouter_version property

Return scouter version used to create DriftProfile

__init__(config, metrics)

Initialize a CustomDriftProfile instance.

Parameters:

Name	Type	Description	Default
config	CustomMetricDriftConfig	The configuration for custom metric drift detection.	required
metrics	list[CustomMetric]	A list of CustomMetric objects representing the metrics to be monitored.	required

Example

config = CustomMetricDriftConfig(...) metrics = [CustomMetric("accuracy", 0.95), CustomMetric("f1_score", 0.88)] profile = CustomDriftProfile(config, metrics, "1.0.0")

Source code in python/scouter/drift/_drift.pyi

def __init__(
    self,
    config: CustomMetricDriftConfig,
    metrics: list[CustomMetric],
):
    """Initialize a CustomDriftProfile instance.

    Args:
        config (CustomMetricDriftConfig):
            The configuration for custom metric drift detection.
        metrics (list[CustomMetric]):
            A list of CustomMetric objects representing the metrics to be monitored.

    Example:
        config = CustomMetricDriftConfig(...)
        metrics = [CustomMetric("accuracy", 0.95), CustomMetric("f1_score", 0.88)]
        profile = CustomDriftProfile(config, metrics, "1.0.0")
    """

__str__()

Sting representation of DriftProfile

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Sting representation of DriftProfile"""

from_file(path) staticmethod

Load drift profile from file

Parameters:

Name	Type	Description	Default
path	Path	Path to the file	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def from_file(path: Path) -> "CustomDriftProfile":
    """Load drift profile from file

    Args:
        path: Path to the file
    """

model_dump()

Return dictionary representation of drift profile

Source code in python/scouter/drift/_drift.pyi

def model_dump(self) -> Dict[str, Any]:
    """Return dictionary representation of drift profile"""

model_dump_json()

Return json representation of drift profile

Source code in python/scouter/drift/_drift.pyi

def model_dump_json(self) -> str:
    """Return json representation of drift profile"""

model_validate(data) staticmethod

Load drift profile from dictionary

Parameters:

Name	Type	Description	Default
data	Dict[str, Any]	DriftProfile dictionary	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def model_validate(data: Dict[str, Any]) -> "CustomDriftProfile":
    """Load drift profile from dictionary

    Args:
        data:
            DriftProfile dictionary
    """

model_validate_json(json_string) staticmethod

Load drift profile from json

Parameters:

Name	Type	Description	Default
json_string	str	JSON string representation of the drift profile	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def model_validate_json(json_string: str) -> "CustomDriftProfile":
    """Load drift profile from json

    Args:
        json_string:
            JSON string representation of the drift profile

    """

save_to_json(path=None)

Save drift profile to json file

Parameters:

Name	Type	Description	Default
path	Optional[Path]	Optional path to save the drift profile. If None, outputs to custom_drift_profile.json	None

Returns:

Type	Description
Path	Path to the saved json file

Source code in python/scouter/drift/_drift.pyi

def save_to_json(self, path: Optional[Path] = None) -> Path:
    """Save drift profile to json file

    Args:
        path:
            Optional path to save the drift profile. If None, outputs to `custom_drift_profile.json`

    Returns:
        Path to the saved json file
    """

update_config_args(space=None, name=None, version=None, alert_config=None)

Inplace operation that updates config args

Parameters:

Name	Type	Description	Default
space	Optional[str]	Model space	None
name	Optional[str]	Model name	None
version	Optional[str]	Model version	None
alert_config	Optional[CustomMetricAlertConfig]	Custom metric alert configuration	None

Returns:

Type	Description
None	None

Source code in python/scouter/drift/_drift.pyi

def update_config_args(
    self,
    space: Optional[str] = None,
    name: Optional[str] = None,
    version: Optional[str] = None,
    alert_config: Optional[CustomMetricAlertConfig] = None,
) -> None:
    """Inplace operation that updates config args

    Args:
        space (Optional[str]):
            Model space
        name (Optional[str]):
            Model name
        version (Optional[str]):
            Model version
        alert_config (Optional[CustomMetricAlertConfig]):
            Custom metric alert configuration

    Returns:
        None
    """

CustomMetric

Source code in python/scouter/drift/_drift.pyi

class CustomMetric:
    def __init__(
        self,
        name: str,
        value: float,
        alert_threshold: AlertThreshold,
        alert_threshold_value: Optional[float] = None,
    ):
        """
        Initialize a custom metric for alerting.

        This class represents a custom metric that uses comparison-based alerting. It applies
        an alert condition to a single metric value.

        Args:
            name (str): The name of the metric being monitored. This should be a
                descriptive identifier for the metric.
            value (float): The current value of the metric.
            alert_threshold (AlertThreshold):
                The condition used to determine when an alert should be triggered.
            alert_threshold_value (Optional[float]):
                The threshold or boundary value used in conjunction with the alert_threshold.
                If supplied, this value will be added or subtracted from the provided metric value to
                determine if an alert should be triggered.

        """

    @property
    def name(self) -> str:
        """Return the metric name"""

    @name.setter
    def name(self, name: str) -> None:
        """Set the metric name"""

    @property
    def value(self) -> float:
        """Return the metric value"""

    @value.setter
    def value(self, value: float) -> None:
        """Set the metric value"""

    @property
    def alert_condition(self) -> CustomMetricAlertCondition:
        """Return the alert_condition"""

    @alert_condition.setter
    def alert_condition(self, alert_condition: CustomMetricAlertCondition) -> None:
        """Set the alert_condition"""

    @property
    def alert_threshold(self) -> AlertThreshold:
        """Return the alert_threshold"""

    @property
    def alert_threshold_value(self) -> Optional[float]:
        """Return the alert_threshold_value"""

    def __str__(self) -> str:
        """Return the string representation of the config."""

alert_condition property writable

Return the alert_condition

alert_threshold property

Return the alert_threshold

alert_threshold_value property

Return the alert_threshold_value

name property writable

Return the metric name

value property writable

Return the metric value

__init__(name, value, alert_threshold, alert_threshold_value=None)

Initialize a custom metric for alerting.

This class represents a custom metric that uses comparison-based alerting. It applies an alert condition to a single metric value.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric being monitored. This should be a descriptive identifier for the metric.	required
value	float	The current value of the metric.	required
alert_threshold	AlertThreshold	The condition used to determine when an alert should be triggered.	required
alert_threshold_value	Optional[float]	The threshold or boundary value used in conjunction with the alert_threshold. If supplied, this value will be added or subtracted from the provided metric value to determine if an alert should be triggered.	None

Source code in python/scouter/drift/_drift.pyi

def __init__(
    self,
    name: str,
    value: float,
    alert_threshold: AlertThreshold,
    alert_threshold_value: Optional[float] = None,
):
    """
    Initialize a custom metric for alerting.

    This class represents a custom metric that uses comparison-based alerting. It applies
    an alert condition to a single metric value.

    Args:
        name (str): The name of the metric being monitored. This should be a
            descriptive identifier for the metric.
        value (float): The current value of the metric.
        alert_threshold (AlertThreshold):
            The condition used to determine when an alert should be triggered.
        alert_threshold_value (Optional[float]):
            The threshold or boundary value used in conjunction with the alert_threshold.
            If supplied, this value will be added or subtracted from the provided metric value to
            determine if an alert should be triggered.

    """

__str__()

Return the string representation of the config.

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Return the string representation of the config."""

CustomMetricDriftConfig

Source code in python/scouter/drift/_drift.pyi

class CustomMetricDriftConfig:
    def __init__(
        self,
        space: str = "__missing__",
        name: str = "__missing__",
        version: str = "0.1.0",
        sample_size: int = 25,
        alert_config: CustomMetricAlertConfig = CustomMetricAlertConfig(),
    ):
        """Initialize drift config
        Args:
            space:
                Model space
            name:
                Model name
            version:
                Model version. Defaults to 0.1.0
            sample_size:
                Sample size
            alert_config:
                Custom metric alert configuration
        """

    @property
    def space(self) -> str:
        """Model space"""

    @space.setter
    def space(self, space: str) -> None:
        """Set model space"""

    @property
    def name(self) -> str:
        """Model Name"""

    @name.setter
    def name(self, name: str) -> None:
        """Set model name"""

    @property
    def version(self) -> str:
        """Model version"""

    @version.setter
    def version(self, version: str) -> None:
        """Set model version"""

    @property
    def drift_type(self) -> DriftType:
        """Drift type"""

    @property
    def alert_config(self) -> CustomMetricAlertConfig:
        """get alert_config"""

    @alert_config.setter
    def alert_config(self, alert_config: CustomMetricAlertConfig) -> None:
        """Set alert_config"""

    @staticmethod
    def load_from_json_file(path: Path) -> "CustomMetricDriftConfig":
        """Load config from json file
        Args:
            path:
                Path to json file to load config from.
        """

    def __str__(self) -> str:
        """Return the string representation of the config."""

    def model_dump_json(self) -> str:
        """Return the json representation of the config."""

    def update_config_args(
        self,
        space: Optional[str] = None,
        name: Optional[str] = None,
        version: Optional[str] = None,
        alert_config: Optional[CustomMetricAlertConfig] = None,
    ) -> None:
        """Inplace operation that updates config args
        Args:
            space:
                Model space
            name:
                Model name
            version:
                Model version
            alert_config:
                Custom metric alert configuration
        """

alert_config property writable

get alert_config

drift_type property

Drift type

name property writable

Model Name

space property writable

Model space

version property writable

Model version

__init__(space='__missing__', name='__missing__', version='0.1.0', sample_size=25, alert_config=CustomMetricAlertConfig())

Initialize drift config Args: space: Model space name: Model name version: Model version. Defaults to 0.1.0 sample_size: Sample size alert_config: Custom metric alert configuration

Source code in python/scouter/drift/_drift.pyi

def __init__(
    self,
    space: str = "__missing__",
    name: str = "__missing__",
    version: str = "0.1.0",
    sample_size: int = 25,
    alert_config: CustomMetricAlertConfig = CustomMetricAlertConfig(),
):
    """Initialize drift config
    Args:
        space:
            Model space
        name:
            Model name
        version:
            Model version. Defaults to 0.1.0
        sample_size:
            Sample size
        alert_config:
            Custom metric alert configuration
    """

__str__()

Return the string representation of the config.

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Return the string representation of the config."""

load_from_json_file(path) staticmethod

Load config from json file Args: path: Path to json file to load config from.

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def load_from_json_file(path: Path) -> "CustomMetricDriftConfig":
    """Load config from json file
    Args:
        path:
            Path to json file to load config from.
    """

model_dump_json()

Return the json representation of the config.

Source code in python/scouter/drift/_drift.pyi

def model_dump_json(self) -> str:
    """Return the json representation of the config."""

update_config_args(space=None, name=None, version=None, alert_config=None)

Inplace operation that updates config args Args: space: Model space name: Model name version: Model version alert_config: Custom metric alert configuration

Source code in python/scouter/drift/_drift.pyi

def update_config_args(
    self,
    space: Optional[str] = None,
    name: Optional[str] = None,
    version: Optional[str] = None,
    alert_config: Optional[CustomMetricAlertConfig] = None,
) -> None:
    """Inplace operation that updates config args
    Args:
        space:
            Model space
        name:
            Model name
        version:
            Model version
        alert_config:
            Custom metric alert configuration
    """

Drifter

Source code in python/scouter/drift/_drift.pyi

class Drifter:
    def __init__(self) -> None:
        """Instantiate Rust Drifter class that is
        used to create monitoring profiles and compute drifts.
        """

    @overload
    def create_drift_profile(
        self,
        data: Any,
        config: SpcDriftConfig,
        data_type: Optional[DataType] = None,
    ) -> SpcDriftProfile:
        """Create a SPC (Statistical process control) drift profile from the provided data.

        Args:
            data:
                Data to create a data profile from. Data can be a numpy array,
                a polars dataframe or a pandas dataframe.

                **Data is expected to not contain any missing values, NaNs or infinities**

            config:
                SpcDriftConfig
            data_type:
                Optional data type. Inferred from data if not provided.

        Returns:
            SpcDriftProfile
        """

    @overload
    def create_drift_profile(
        self,
        data: Any,
        data_type: Optional[DataType] = None,
    ) -> SpcDriftProfile:
        """Create a SPC (Statistical process control) drift profile from the provided data.

        Args:
            data:
                Data to create a data profile from. Data can be a numpy array,
                a polars dataframe or a pandas dataframe.

                **Data is expected to not contain any missing values, NaNs or infinities**

            config:
                SpcDriftConfig
            data_type:
                Optional data type. Inferred from data if not provided.

        Returns:
            SpcDriftProfile
        """

    @overload
    def create_drift_profile(
        self,
        data: Any,
        config: PsiDriftConfig,
        data_type: Optional[DataType] = None,
    ) -> PsiDriftProfile:
        """Create a PSI (population stability index) drift profile from the provided data.

        Args:
            data:
                Data to create a data profile from. Data can be a numpy array,
                a polars dataframe or a pandas dataframe.

                **Data is expected to not contain any missing values, NaNs or infinities**

            config:
                PsiDriftConfig
            data_type:
                Optional data type. Inferred from data if not provided.

        Returns:
            PsiDriftProfile
        """

    @overload
    def create_drift_profile(
        self,
        data: Union[CustomMetric, List[CustomMetric]],
        config: CustomMetricDriftConfig,
        data_type: Optional[DataType] = None,
    ) -> CustomDriftProfile:
        """Create a custom drift profile from data.

        Args:
            data:
                CustomMetric or list of CustomMetric.
            config:
                CustomMetricDriftConfig
            data_type:
                Optional data type. Inferred from data if not provided.

        Returns:
            CustomDriftProfile
        """

    def create_drift_profile(  # type: ignore
        self,
        data: Any,
        config: Optional[Union[SpcDriftConfig, PsiDriftConfig, CustomMetricDriftConfig]] = None,
        data_type: Optional[DataType] = None,
    ) -> Union[SpcDriftProfile, PsiDriftProfile, CustomDriftProfile]:
        """Create a drift profile from data.

        Args:
            data:
                Data to create a data profile from. Data can be a numpy array,
                a polars dataframe, pandas dataframe or a list of CustomMetric if creating
                a custom metric profile.

                **Data is expected to not contain any missing values, NaNs or infinities**

            config:
                Drift config that will be used for monitoring
            data_type:
                Optional data type. Inferred from data if not provided.

        Returns:
            SpcDriftProfile, PsiDriftProfile or CustomDriftProfile
        """

    def create_llm_drift_profile(
        self,
        config: LLMDriftConfig,
        metrics: List[LLMMetric],
        workflow: Optional[Workflow] = None,
    ) -> LLMDriftProfile:
        """Initialize a LLMDriftProfile for LLM evaluation and drift detection.

        LLM evaluations are run asynchronously on the scouter server.

        Logic flow:
            1. If only metrics are provided, a workflow will be created automatically
               from the metrics. In this case a prompt is required for each metric.
            2. If a workflow is provided, it will be parsed and validated for compatibility:
               - A list of metrics to evaluate workflow output must be provided
               - Metric names must correspond to the final task names in the workflow

        Baseline metrics and thresholds will be extracted from the LLMMetric objects.

        Args:
            config (LLMDriftConfig):
                The configuration for the LLM drift profile containing space, name,
                version, and alert settings.
            metrics (list[LLMMetric]):
                A list of LLMMetric objects representing the metrics to be monitored.
                Each metric defines evaluation criteria and alert thresholds.
            workflow (Optional[Workflow]):
                Optional custom workflow for advanced evaluation scenarios. If provided,
                the workflow will be validated to ensure proper parameter and response
                type configuration.

        Returns:
            LLMDriftProfile: Configured profile ready for LLM drift monitoring.

        Raises:
            ProfileError: If workflow validation fails, metrics are empty when no
                workflow is provided, or if workflow tasks don't match metric names.

        Examples:
            Basic usage with metrics only:

            >>> config = LLMDriftConfig("my_space", "my_model", "1.0")
            >>> metrics = [
            ...     LLMMetric("accuracy", 0.95, AlertThreshold.Above, 0.1, prompt),
            ...     LLMMetric("relevance", 0.85, AlertThreshold.Below, 0.2, prompt2)
            ... ]
            >>> profile = Drifter().create_llm_drift_profile(config, metrics)

            Advanced usage with custom workflow:

            >>> workflow = create_custom_workflow()  # Your custom workflow
            >>> metrics = [LLMMetric("final_task", 0.9, AlertThreshold.Above)]
            >>> profile = Drifter().create_llm_drift_profile(config, metrics, workflow)

        Note:
            - When using custom workflows, ensure final tasks have Score response types
            - Initial workflow tasks must include "input" and/or "response" parameters
            - All metric names must match corresponding workflow task names
        """

    @overload
    def compute_drift(
        self,
        data: Any,
        drift_profile: SpcDriftProfile,
        data_type: Optional[DataType] = None,
    ) -> SpcDriftMap:
        """Create a drift map from data.

        Args:
            data:
                Data to create a data profile from. Data can be a numpy array,
                a polars dataframe or a pandas dataframe.
            drift_profile:
                Drift profile to use to compute drift map
            data_type:
                Optional data type. Inferred from data if not provided.

        Returns:
            SpcDriftMap
        """

    @overload
    def compute_drift(
        self,
        data: Any,
        drift_profile: PsiDriftProfile,
        data_type: Optional[DataType] = None,
    ) -> PsiDriftMap:
        """Create a drift map from data.

        Args:
            data:
                Data to create a data profile from. Data can be a numpy array,
                a polars dataframe or a pandas dataframe.
            drift_profile:
                Drift profile to use to compute drift map
            data_type:
                Optional data type. Inferred from data if not provided.

        Returns:
            PsiDriftMap
        """

    @overload
    def compute_drift(
        self,
        data: Union[LLMRecord, List[LLMRecord]],
        drift_profile: LLMDriftProfile,
        data_type: Optional[DataType] = None,
    ) -> LLMDriftMap:
        """Create a drift map from data.

        Args:
            data:

            drift_profile:
                Drift profile to use to compute drift map
            data_type:
                Optional data type. Inferred from data if not provided.

        Returns:
            LLMDriftMap
        """

    def compute_drift(  # type: ignore
        self,
        data: Any,
        drift_profile: Union[SpcDriftProfile, PsiDriftProfile, LLMDriftProfile],
        data_type: Optional[DataType] = None,
    ) -> Union[SpcDriftMap, PsiDriftMap, LLMDriftMap]:
        """Create a drift map from data.

        Args:
            data:
                Data to create a data profile from. Data can be a numpy array,
                a polars dataframe or a pandas dataframe.
            drift_profile:
                Drift profile to use to compute drift map
            data_type:
                Optional data type. Inferred from data if not provided.

        Returns:
            SpcDriftMap, PsiDriftMap or LLMDriftMap
        """

__init__()

Instantiate Rust Drifter class that is used to create monitoring profiles and compute drifts.

Source code in python/scouter/drift/_drift.pyi

def __init__(self) -> None:
    """Instantiate Rust Drifter class that is
    used to create monitoring profiles and compute drifts.
    """

compute_drift(data, drift_profile, data_type=None)

compute_drift(
    data: Any,
    drift_profile: SpcDriftProfile,
    data_type: Optional[DataType] = None,
) -> SpcDriftMap

compute_drift(
    data: Any,
    drift_profile: PsiDriftProfile,
    data_type: Optional[DataType] = None,
) -> PsiDriftMap

compute_drift(
    data: Union[LLMRecord, List[LLMRecord]],
    drift_profile: LLMDriftProfile,
    data_type: Optional[DataType] = None,
) -> LLMDriftMap

Create a drift map from data.

Parameters:

Name	Type	Description	Default
data	Any	Data to create a data profile from. Data can be a numpy array, a polars dataframe or a pandas dataframe.	required
drift_profile	Union[SpcDriftProfile, PsiDriftProfile, LLMDriftProfile]	Drift profile to use to compute drift map	required
data_type	Optional[DataType]	Optional data type. Inferred from data if not provided.	None

Returns:

Type	Description
Union[SpcDriftMap, PsiDriftMap, LLMDriftMap]	SpcDriftMap, PsiDriftMap or LLMDriftMap

Source code in python/scouter/drift/_drift.pyi

def compute_drift(  # type: ignore
    self,
    data: Any,
    drift_profile: Union[SpcDriftProfile, PsiDriftProfile, LLMDriftProfile],
    data_type: Optional[DataType] = None,
) -> Union[SpcDriftMap, PsiDriftMap, LLMDriftMap]:
    """Create a drift map from data.

    Args:
        data:
            Data to create a data profile from. Data can be a numpy array,
            a polars dataframe or a pandas dataframe.
        drift_profile:
            Drift profile to use to compute drift map
        data_type:
            Optional data type. Inferred from data if not provided.

    Returns:
        SpcDriftMap, PsiDriftMap or LLMDriftMap
    """

create_drift_profile(data, config=None, data_type=None)

create_drift_profile(
    data: Any,
    config: SpcDriftConfig,
    data_type: Optional[DataType] = None,
) -> SpcDriftProfile

create_drift_profile(
    data: Any, data_type: Optional[DataType] = None
) -> SpcDriftProfile

create_drift_profile(
    data: Any,
    config: PsiDriftConfig,
    data_type: Optional[DataType] = None,
) -> PsiDriftProfile

create_drift_profile(
    data: Union[CustomMetric, List[CustomMetric]],
    config: CustomMetricDriftConfig,
    data_type: Optional[DataType] = None,
) -> CustomDriftProfile

Create a drift profile from data.

Parameters:

Name	Type	Description	Default
data	Any	Data to create a data profile from. Data can be a numpy array, a polars dataframe, pandas dataframe or a list of CustomMetric if creating a custom metric profile. Data is expected to not contain any missing values, NaNs or infinities	required
config	Optional[Union[SpcDriftConfig, PsiDriftConfig, CustomMetricDriftConfig]]	Drift config that will be used for monitoring	None
data_type	Optional[DataType]	Optional data type. Inferred from data if not provided.	None

Returns:

Type	Description
Union[SpcDriftProfile, PsiDriftProfile, CustomDriftProfile]	SpcDriftProfile, PsiDriftProfile or CustomDriftProfile

Source code in python/scouter/drift/_drift.pyi

def create_drift_profile(  # type: ignore
    self,
    data: Any,
    config: Optional[Union[SpcDriftConfig, PsiDriftConfig, CustomMetricDriftConfig]] = None,
    data_type: Optional[DataType] = None,
) -> Union[SpcDriftProfile, PsiDriftProfile, CustomDriftProfile]:
    """Create a drift profile from data.

    Args:
        data:
            Data to create a data profile from. Data can be a numpy array,
            a polars dataframe, pandas dataframe or a list of CustomMetric if creating
            a custom metric profile.

            **Data is expected to not contain any missing values, NaNs or infinities**

        config:
            Drift config that will be used for monitoring
        data_type:
            Optional data type. Inferred from data if not provided.

    Returns:
        SpcDriftProfile, PsiDriftProfile or CustomDriftProfile
    """

create_llm_drift_profile(config, metrics, workflow=None)

Initialize a LLMDriftProfile for LLM evaluation and drift detection.

LLM evaluations are run asynchronously on the scouter server.

Logic flow

1. If only metrics are provided, a workflow will be created automatically from the metrics. In this case a prompt is required for each metric.
2. If a workflow is provided, it will be parsed and validated for compatibility:
3. A list of metrics to evaluate workflow output must be provided
4. Metric names must correspond to the final task names in the workflow

Baseline metrics and thresholds will be extracted from the LLMMetric objects.

Parameters:

Name	Type	Description	Default
config	LLMDriftConfig	The configuration for the LLM drift profile containing space, name, version, and alert settings.	required
metrics	list[LLMMetric]	A list of LLMMetric objects representing the metrics to be monitored. Each metric defines evaluation criteria and alert thresholds.	required
workflow	Optional[Workflow]	Optional custom workflow for advanced evaluation scenarios. If provided, the workflow will be validated to ensure proper parameter and response type configuration.	None

Returns:

Name	Type	Description
LLMDriftProfile	LLMDriftProfile	Configured profile ready for LLM drift monitoring.

Raises:

Type	Description
ProfileError	If workflow validation fails, metrics are empty when no workflow is provided, or if workflow tasks don't match metric names.

Examples:

Basic usage with metrics only:

>>> config = LLMDriftConfig("my_space", "my_model", "1.0")
>>> metrics = [
...     LLMMetric("accuracy", 0.95, AlertThreshold.Above, 0.1, prompt),
...     LLMMetric("relevance", 0.85, AlertThreshold.Below, 0.2, prompt2)
... ]
>>> profile = Drifter().create_llm_drift_profile(config, metrics)

Advanced usage with custom workflow:

>>> workflow = create_custom_workflow()  # Your custom workflow
>>> metrics = [LLMMetric("final_task", 0.9, AlertThreshold.Above)]
>>> profile = Drifter().create_llm_drift_profile(config, metrics, workflow)

Note

• When using custom workflows, ensure final tasks have Score response types
• Initial workflow tasks must include "input" and/or "response" parameters
• All metric names must match corresponding workflow task names

Source code in python/scouter/drift/_drift.pyi

def create_llm_drift_profile(
    self,
    config: LLMDriftConfig,
    metrics: List[LLMMetric],
    workflow: Optional[Workflow] = None,
) -> LLMDriftProfile:
    """Initialize a LLMDriftProfile for LLM evaluation and drift detection.

    LLM evaluations are run asynchronously on the scouter server.

    Logic flow:
        1. If only metrics are provided, a workflow will be created automatically
           from the metrics. In this case a prompt is required for each metric.
        2. If a workflow is provided, it will be parsed and validated for compatibility:
           - A list of metrics to evaluate workflow output must be provided
           - Metric names must correspond to the final task names in the workflow

    Baseline metrics and thresholds will be extracted from the LLMMetric objects.

    Args:
        config (LLMDriftConfig):
            The configuration for the LLM drift profile containing space, name,
            version, and alert settings.
        metrics (list[LLMMetric]):
            A list of LLMMetric objects representing the metrics to be monitored.
            Each metric defines evaluation criteria and alert thresholds.
        workflow (Optional[Workflow]):
            Optional custom workflow for advanced evaluation scenarios. If provided,
            the workflow will be validated to ensure proper parameter and response
            type configuration.

    Returns:
        LLMDriftProfile: Configured profile ready for LLM drift monitoring.

    Raises:
        ProfileError: If workflow validation fails, metrics are empty when no
            workflow is provided, or if workflow tasks don't match metric names.

    Examples:
        Basic usage with metrics only:

        >>> config = LLMDriftConfig("my_space", "my_model", "1.0")
        >>> metrics = [
        ...     LLMMetric("accuracy", 0.95, AlertThreshold.Above, 0.1, prompt),
        ...     LLMMetric("relevance", 0.85, AlertThreshold.Below, 0.2, prompt2)
        ... ]
        >>> profile = Drifter().create_llm_drift_profile(config, metrics)

        Advanced usage with custom workflow:

        >>> workflow = create_custom_workflow()  # Your custom workflow
        >>> metrics = [LLMMetric("final_task", 0.9, AlertThreshold.Above)]
        >>> profile = Drifter().create_llm_drift_profile(config, metrics, workflow)

    Note:
        - When using custom workflows, ensure final tasks have Score response types
        - Initial workflow tasks must include "input" and/or "response" parameters
        - All metric names must match corresponding workflow task names
    """

FeatureDrift

Source code in python/scouter/drift/_drift.pyi

class FeatureDrift:
    @property
    def samples(self) -> List[float]:
        """Return list of samples"""

    @property
    def drift(self) -> List[float]:
        """Return list of drift values"""

    def __str__(self) -> str:
        """Return string representation of feature drift"""

drift property

Return list of drift values

samples property

Return list of samples

__str__()

Return string representation of feature drift

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Return string representation of feature drift"""

FeatureMap

Source code in python/scouter/drift/_drift.pyi

class FeatureMap:
    @property
    def features(self) -> Dict[str, Dict[str, int]]:
        """Return the feature map."""

    def __str__(self) -> str:
        """Return the string representation of the feature map."""

features property

Return the feature map.

__str__()

Return the string representation of the feature map.

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Return the string representation of the feature map."""

LLMDriftConfig

Source code in python/scouter/drift/_drift.pyi

class LLMDriftConfig:
    def __init__(
        self,
        space: str = "__missing__",
        name: str = "__missing__",
        version: str = "0.1.0",
        sample_rate: int = 5,
        alert_config: LLMAlertConfig = LLMAlertConfig(),
    ):
        """Initialize drift config
        Args:
            space:
                Space to associate with the config
            name:
                Name to associate with the config
            version:
                Version to associate with the config. Defaults to 0.1.0
            sample_rate:
                Sample rate for LLM drift detection. Defaults to 5.
            alert_config:
                Custom metric alert configuration
        """

    @property
    def space(self) -> str:
        """Model space"""

    @space.setter
    def space(self, space: str) -> None:
        """Set model space"""

    @property
    def name(self) -> str:
        """Model Name"""

    @name.setter
    def name(self, name: str) -> None:
        """Set model name"""

    @property
    def version(self) -> str:
        """Model version"""

    @version.setter
    def version(self, version: str) -> None:
        """Set model version"""

    @property
    def drift_type(self) -> DriftType:
        """Drift type"""

    @property
    def alert_config(self) -> LLMAlertConfig:
        """get alert_config"""

    @alert_config.setter
    def alert_config(self, alert_config: LLMAlertConfig) -> None:
        """Set alert_config"""

    @staticmethod
    def load_from_json_file(path: Path) -> "LLMDriftConfig":
        """Load config from json file
        Args:
            path:
                Path to json file to load config from.
        """

    def __str__(self) -> str:
        """Return the string representation of the config."""

    def model_dump_json(self) -> str:
        """Return the json representation of the config."""

    def update_config_args(
        self,
        space: Optional[str] = None,
        name: Optional[str] = None,
        version: Optional[str] = None,
        alert_config: Optional[LLMAlertConfig] = None,
    ) -> None:
        """Inplace operation that updates config args
        Args:
            space:
                Space to associate with the config
            name:
                Name to associate with the config
            version:
                Version to associate with the config
            alert_config:
                LLM alert configuration
        """

alert_config property writable

get alert_config

drift_type property

Drift type

name property writable

Model Name

space property writable

Model space

version property writable

Model version

__init__(space='__missing__', name='__missing__', version='0.1.0', sample_rate=5, alert_config=LLMAlertConfig())

Initialize drift config Args: space: Space to associate with the config name: Name to associate with the config version: Version to associate with the config. Defaults to 0.1.0 sample_rate: Sample rate for LLM drift detection. Defaults to 5. alert_config: Custom metric alert configuration

Source code in python/scouter/drift/_drift.pyi

def __init__(
    self,
    space: str = "__missing__",
    name: str = "__missing__",
    version: str = "0.1.0",
    sample_rate: int = 5,
    alert_config: LLMAlertConfig = LLMAlertConfig(),
):
    """Initialize drift config
    Args:
        space:
            Space to associate with the config
        name:
            Name to associate with the config
        version:
            Version to associate with the config. Defaults to 0.1.0
        sample_rate:
            Sample rate for LLM drift detection. Defaults to 5.
        alert_config:
            Custom metric alert configuration
    """

__str__()

Return the string representation of the config.

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Return the string representation of the config."""

load_from_json_file(path) staticmethod

Load config from json file Args: path: Path to json file to load config from.

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def load_from_json_file(path: Path) -> "LLMDriftConfig":
    """Load config from json file
    Args:
        path:
            Path to json file to load config from.
    """

model_dump_json()

Return the json representation of the config.

Source code in python/scouter/drift/_drift.pyi

def model_dump_json(self) -> str:
    """Return the json representation of the config."""

update_config_args(space=None, name=None, version=None, alert_config=None)

Inplace operation that updates config args Args: space: Space to associate with the config name: Name to associate with the config version: Version to associate with the config alert_config: LLM alert configuration

Source code in python/scouter/drift/_drift.pyi

def update_config_args(
    self,
    space: Optional[str] = None,
    name: Optional[str] = None,
    version: Optional[str] = None,
    alert_config: Optional[LLMAlertConfig] = None,
) -> None:
    """Inplace operation that updates config args
    Args:
        space:
            Space to associate with the config
        name:
            Name to associate with the config
        version:
            Version to associate with the config
        alert_config:
            LLM alert configuration
    """

LLMDriftMap

Source code in python/scouter/drift/_drift.pyi

class LLMDriftMap:
    @property
    def records(self) -> List[LLMMetricRecord]:
        """Return the list of LLM records."""

    def __str__(self): ...

records property

Return the list of LLM records.

LLMDriftProfile

Source code in python/scouter/drift/_drift.pyi

class LLMDriftProfile:
    def __init__(
        self,
        config: LLMDriftConfig,
        metrics: list[LLMMetric],
        workflow: Optional[Workflow] = None,
    ):
        """Initialize a LLMDriftProfile for LLM evaluation and drift detection.

        LLM evaluations are run asynchronously on the scouter server.

        Logic flow:
            1. If only metrics are provided, a workflow will be created automatically
               from the metrics. In this case a prompt is required for each metric.
            2. If a workflow is provided, it will be parsed and validated for compatibility:
               - A list of metrics to evaluate workflow output must be provided
               - Metric names must correspond to the final task names in the workflow

        Baseline metrics and thresholds will be extracted from the LLMMetric objects.

        Args:
            config (LLMDriftConfig):
                The configuration for the LLM drift profile containing space, name,
                version, and alert settings.
            metrics (list[LLMMetric]):
                A list of LLMMetric objects representing the metrics to be monitored.
                Each metric defines evaluation criteria and alert thresholds.
            workflow (Optional[Workflow]):
                Optional custom workflow for advanced evaluation scenarios. If provided,
                the workflow will be validated to ensure proper parameter and response
                type configuration.

        Returns:
            LLMDriftProfile: Configured profile ready for LLM drift monitoring.

        Raises:
            ProfileError: If workflow validation fails, metrics are empty when no
                workflow is provided, or if workflow tasks don't match metric names.

        Examples:
            Basic usage with metrics only:

            >>> config = LLMDriftConfig("my_space", "my_model", "1.0")
            >>> metrics = [
            ...     LLMMetric("accuracy", 0.95, AlertThreshold.Above, 0.1, prompt),
            ...     LLMMetric("relevance", 0.85, AlertThreshold.Below, 0.2, prompt2)
            ... ]
            >>> profile = LLMDriftProfile(config, metrics)

            Advanced usage with custom workflow:

            >>> workflow = create_custom_workflow()  # Your custom workflow
            >>> metrics = [LLMMetric("final_task", 0.9, AlertThreshold.Above)]
            >>> profile = LLMDriftProfile(config, metrics, workflow)

        Note:
            - When using custom workflows, ensure final tasks have Score response types
            - Initial workflow tasks must include "input" and/or "response" parameters
            - All metric names must match corresponding workflow task names
        """

    @property
    def config(self) -> LLMDriftConfig:
        """Return the drift config"""

    @property
    def metrics(self) -> List[LLMMetric]:
        """Return LLM metrics and their corresponding values"""

    @property
    def scouter_version(self) -> str:
        """Return scouter version used to create DriftProfile"""

    def __str__(self) -> str:
        """String representation of LLMDriftProfile"""

    def model_dump_json(self) -> str:
        """Return json representation of drift profile"""

    def model_dump(self) -> Dict[str, Any]:
        """Return dictionary representation of drift profile"""

    def save_to_json(self, path: Optional[Path] = None) -> Path:
        """Save drift profile to json file

        Args:
            path: Optional path to save the json file. If not provided, a default path will be used.

        Returns:
            Path to the saved json file.
        """

    @staticmethod
    def model_validate(data: Dict[str, Any]) -> "LLMDriftProfile":
        """Load drift profile from dictionary

        Args:
            data:
                DriftProfile dictionary
        """

    @staticmethod
    def model_validate_json(json_string: str) -> "LLMDriftProfile":
        """Load drift profile from json

        Args:
            json_string:
                JSON string representation of the drift profile
        """

    @staticmethod
    def from_file(path: Path) -> "LLMDriftProfile":
        """Load drift profile from file

        Args:
            path: Path to the json file

        Returns:
            LLMDriftProfile
        """

    def update_config_args(
        self,
        space: Optional[str] = None,
        name: Optional[str] = None,
        version: Optional[str] = None,
        sample_size: Optional[int] = None,
        alert_config: Optional[LLMAlertConfig] = None,
    ) -> None:
        """Inplace operation that updates config args

        Args:
            name:
                Model name
            space:
                Model space
            version:
                Model version
            sample_size:
                Sample size
            alert_config:
                Alert configuration
        """

config property

Return the drift config

metrics property

Return LLM metrics and their corresponding values

scouter_version property

Return scouter version used to create DriftProfile

__init__(config, metrics, workflow=None)

Initialize a LLMDriftProfile for LLM evaluation and drift detection.

LLM evaluations are run asynchronously on the scouter server.

Logic flow

1. If only metrics are provided, a workflow will be created automatically from the metrics. In this case a prompt is required for each metric.
2. If a workflow is provided, it will be parsed and validated for compatibility:
3. A list of metrics to evaluate workflow output must be provided
4. Metric names must correspond to the final task names in the workflow

Baseline metrics and thresholds will be extracted from the LLMMetric objects.

Parameters:

Name	Type	Description	Default
config	LLMDriftConfig	The configuration for the LLM drift profile containing space, name, version, and alert settings.	required
metrics	list[LLMMetric]	A list of LLMMetric objects representing the metrics to be monitored. Each metric defines evaluation criteria and alert thresholds.	required
workflow	Optional[Workflow]	Optional custom workflow for advanced evaluation scenarios. If provided, the workflow will be validated to ensure proper parameter and response type configuration.	None

Returns:

Name	Type	Description
LLMDriftProfile		Configured profile ready for LLM drift monitoring.

Raises:

Type	Description
ProfileError	If workflow validation fails, metrics are empty when no workflow is provided, or if workflow tasks don't match metric names.

Examples:

Basic usage with metrics only:

>>> config = LLMDriftConfig("my_space", "my_model", "1.0")
>>> metrics = [
...     LLMMetric("accuracy", 0.95, AlertThreshold.Above, 0.1, prompt),
...     LLMMetric("relevance", 0.85, AlertThreshold.Below, 0.2, prompt2)
... ]
>>> profile = LLMDriftProfile(config, metrics)

Advanced usage with custom workflow:

>>> workflow = create_custom_workflow()  # Your custom workflow
>>> metrics = [LLMMetric("final_task", 0.9, AlertThreshold.Above)]
>>> profile = LLMDriftProfile(config, metrics, workflow)

Note

• When using custom workflows, ensure final tasks have Score response types
• Initial workflow tasks must include "input" and/or "response" parameters
• All metric names must match corresponding workflow task names

Source code in python/scouter/drift/_drift.pyi

def __init__(
    self,
    config: LLMDriftConfig,
    metrics: list[LLMMetric],
    workflow: Optional[Workflow] = None,
):
    """Initialize a LLMDriftProfile for LLM evaluation and drift detection.

    LLM evaluations are run asynchronously on the scouter server.

    Logic flow:
        1. If only metrics are provided, a workflow will be created automatically
           from the metrics. In this case a prompt is required for each metric.
        2. If a workflow is provided, it will be parsed and validated for compatibility:
           - A list of metrics to evaluate workflow output must be provided
           - Metric names must correspond to the final task names in the workflow

    Baseline metrics and thresholds will be extracted from the LLMMetric objects.

    Args:
        config (LLMDriftConfig):
            The configuration for the LLM drift profile containing space, name,
            version, and alert settings.
        metrics (list[LLMMetric]):
            A list of LLMMetric objects representing the metrics to be monitored.
            Each metric defines evaluation criteria and alert thresholds.
        workflow (Optional[Workflow]):
            Optional custom workflow for advanced evaluation scenarios. If provided,
            the workflow will be validated to ensure proper parameter and response
            type configuration.

    Returns:
        LLMDriftProfile: Configured profile ready for LLM drift monitoring.

    Raises:
        ProfileError: If workflow validation fails, metrics are empty when no
            workflow is provided, or if workflow tasks don't match metric names.

    Examples:
        Basic usage with metrics only:

        >>> config = LLMDriftConfig("my_space", "my_model", "1.0")
        >>> metrics = [
        ...     LLMMetric("accuracy", 0.95, AlertThreshold.Above, 0.1, prompt),
        ...     LLMMetric("relevance", 0.85, AlertThreshold.Below, 0.2, prompt2)
        ... ]
        >>> profile = LLMDriftProfile(config, metrics)

        Advanced usage with custom workflow:

        >>> workflow = create_custom_workflow()  # Your custom workflow
        >>> metrics = [LLMMetric("final_task", 0.9, AlertThreshold.Above)]
        >>> profile = LLMDriftProfile(config, metrics, workflow)

    Note:
        - When using custom workflows, ensure final tasks have Score response types
        - Initial workflow tasks must include "input" and/or "response" parameters
        - All metric names must match corresponding workflow task names
    """

__str__()

String representation of LLMDriftProfile

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """String representation of LLMDriftProfile"""

from_file(path) staticmethod

Load drift profile from file

Parameters:

Name	Type	Description	Default
path	Path	Path to the json file	required

Returns:

Type	Description
LLMDriftProfile	LLMDriftProfile

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def from_file(path: Path) -> "LLMDriftProfile":
    """Load drift profile from file

    Args:
        path: Path to the json file

    Returns:
        LLMDriftProfile
    """

model_dump()

Return dictionary representation of drift profile

Source code in python/scouter/drift/_drift.pyi

def model_dump(self) -> Dict[str, Any]:
    """Return dictionary representation of drift profile"""

model_dump_json()

Return json representation of drift profile

Source code in python/scouter/drift/_drift.pyi

def model_dump_json(self) -> str:
    """Return json representation of drift profile"""

model_validate(data) staticmethod

Load drift profile from dictionary

Parameters:

Name	Type	Description	Default
data	Dict[str, Any]	DriftProfile dictionary	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def model_validate(data: Dict[str, Any]) -> "LLMDriftProfile":
    """Load drift profile from dictionary

    Args:
        data:
            DriftProfile dictionary
    """

model_validate_json(json_string) staticmethod

Load drift profile from json

Parameters:

Name	Type	Description	Default
json_string	str	JSON string representation of the drift profile	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def model_validate_json(json_string: str) -> "LLMDriftProfile":
    """Load drift profile from json

    Args:
        json_string:
            JSON string representation of the drift profile
    """

save_to_json(path=None)

Save drift profile to json file

Parameters:

Name	Type	Description	Default
path	Optional[Path]	Optional path to save the json file. If not provided, a default path will be used.	None

Returns:

Type	Description
Path	Path to the saved json file.

Source code in python/scouter/drift/_drift.pyi

def save_to_json(self, path: Optional[Path] = None) -> Path:
    """Save drift profile to json file

    Args:
        path: Optional path to save the json file. If not provided, a default path will be used.

    Returns:
        Path to the saved json file.
    """

update_config_args(space=None, name=None, version=None, sample_size=None, alert_config=None)

Inplace operation that updates config args

Parameters:

Name	Type	Description	Default
name	Optional[str]	Model name	None
space	Optional[str]	Model space	None
version	Optional[str]	Model version	None
sample_size	Optional[int]	Sample size	None
alert_config	Optional[LLMAlertConfig]	Alert configuration	None

Source code in python/scouter/drift/_drift.pyi

def update_config_args(
    self,
    space: Optional[str] = None,
    name: Optional[str] = None,
    version: Optional[str] = None,
    sample_size: Optional[int] = None,
    alert_config: Optional[LLMAlertConfig] = None,
) -> None:
    """Inplace operation that updates config args

    Args:
        name:
            Model name
        space:
            Model space
        version:
            Model version
        sample_size:
            Sample size
        alert_config:
            Alert configuration
    """

LLMMetric

Metric for monitoring LLM performance.

Source code in python/scouter/drift/_drift.pyi

class LLMMetric:
    """Metric for monitoring LLM performance."""

    def __init__(
        self,
        name: str,
        value: float,
        alert_threshold: AlertThreshold,
        alert_threshold_value: Optional[float] = None,
        prompt: Optional[Prompt] = None,
    ):
        """
        Initialize a metric for monitoring LLM performance.

        Args:
            name (str):
                The name of the metric being monitored. This should be a
                descriptive identifier for the metric.
            value (float):
                The current value of the metric.
            alert_threshold (AlertThreshold):
                The condition used to determine when an alert should be triggered.
            alert_threshold_value (Optional[float]):
                The threshold or boundary value used in conjunction with the alert_threshold.
                If supplied, this value will be added or subtracted from the provided metric value to
                determine if an alert should be triggered.
            prompt (Optional[Prompt]):
                Optional prompt associated with the metric. This can be used to provide context or
                additional information about the metric being monitored. If creating an LLM drift profile
                from a pre-defined workflow, this can be none.
        """

    @property
    def name(self) -> str:
        """Return the metric name"""

    @property
    def value(self) -> float:
        """Return the metric value"""

    @property
    def prompt(self) -> Optional[Prompt]:
        """Return the prompt associated with the metric"""

    @property
    def alert_threshold(self) -> AlertThreshold:
        """Return the alert_threshold"""

    @property
    def alert_threshold_value(self) -> Optional[float]:
        """Return the alert_threshold_value"""

alert_threshold property

Return the alert_threshold

alert_threshold_value property

Return the alert_threshold_value

name property

Return the metric name

prompt property

Return the prompt associated with the metric

value property

Return the metric value

__init__(name, value, alert_threshold, alert_threshold_value=None, prompt=None)

Initialize a metric for monitoring LLM performance.

Parameters:

Name	Type	Description	Default
name	str	The name of the metric being monitored. This should be a descriptive identifier for the metric.	required
value	float	The current value of the metric.	required
alert_threshold	AlertThreshold	The condition used to determine when an alert should be triggered.	required
alert_threshold_value	Optional[float]	The threshold or boundary value used in conjunction with the alert_threshold. If supplied, this value will be added or subtracted from the provided metric value to determine if an alert should be triggered.	None
prompt	Optional[Prompt]	Optional prompt associated with the metric. This can be used to provide context or additional information about the metric being monitored. If creating an LLM drift profile from a pre-defined workflow, this can be none.	None

Source code in python/scouter/drift/_drift.pyi

def __init__(
    self,
    name: str,
    value: float,
    alert_threshold: AlertThreshold,
    alert_threshold_value: Optional[float] = None,
    prompt: Optional[Prompt] = None,
):
    """
    Initialize a metric for monitoring LLM performance.

    Args:
        name (str):
            The name of the metric being monitored. This should be a
            descriptive identifier for the metric.
        value (float):
            The current value of the metric.
        alert_threshold (AlertThreshold):
            The condition used to determine when an alert should be triggered.
        alert_threshold_value (Optional[float]):
            The threshold or boundary value used in conjunction with the alert_threshold.
            If supplied, this value will be added or subtracted from the provided metric value to
            determine if an alert should be triggered.
        prompt (Optional[Prompt]):
            Optional prompt associated with the metric. This can be used to provide context or
            additional information about the metric being monitored. If creating an LLM drift profile
            from a pre-defined workflow, this can be none.
    """

LLMMetricRecord

Source code in python/scouter/drift/_drift.pyi

class LLMMetricRecord:
    @property
    def record_uid(self) -> str:
        """Return the record id"""

    @property
    def created_at(self) -> datetime:
        """Return the timestamp when the record was created"""

    @property
    def space(self) -> str:
        """Return the space associated with the record"""

    @property
    def name(self) -> str:
        """Return the name associated with the record"""

    @property
    def version(self) -> str:
        """Return the version associated with the record"""

    @property
    def metric(self) -> str:
        """Return the name of the metric associated with the record"""

    @property
    def value(self) -> float:
        """Return the value of the metric associated with the record"""

    def __str__(self) -> str:
        """Return the string representation of the record"""

created_at property

Return the timestamp when the record was created

metric property

Return the name of the metric associated with the record

name property

Return the name associated with the record

record_uid property

Return the record id

space property

Return the space associated with the record

value property

Return the value of the metric associated with the record

version property

Return the version associated with the record

__str__()

Return the string representation of the record

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Return the string representation of the record"""

Prompt

Bases: Protocol

Potato Head Prompt Protocol

Source code in python/scouter/drift/_drift.pyi

class Prompt(Protocol):
    """Potato Head Prompt Protocol"""

PsiDriftConfig

Source code in python/scouter/drift/_drift.pyi

class PsiDriftConfig:
    def __init__(
        self,
        space: str = "__missing__",
        name: str = "__missing__",
        version: str = "0.1.0",
        alert_config: PsiAlertConfig = PsiAlertConfig(),
        config_path: Optional[Path] = None,
        categorical_features: Optional[list[str]] = None,
    ):
        """Initialize monitor config

        Args:
            space:
                Model space
            name:
                Model name
            version:
                Model version. Defaults to 0.1.0
            alert_config:
                Alert configuration
            config_path:
                Optional path to load config from.
            categorical_features:
                List of features to treat as categorical for PSI calculation.
        """

    @property
    def name(self) -> str:
        """Model Name"""

    @name.setter
    def name(self, name: str) -> None:
        """Set model name"""

    @property
    def space(self) -> str:
        """Model space"""

    @space.setter
    def space(self, space: str) -> None:
        """Set model space"""

    @property
    def version(self) -> str:
        """Model version"""

    @version.setter
    def version(self, version: str) -> None:
        """Set model version"""

    @property
    def feature_map(self) -> Optional[FeatureMap]:
        """Feature map"""

    @property
    def alert_config(self) -> PsiAlertConfig:
        """Alert configuration"""

    @alert_config.setter
    def alert_config(self, alert_config: PsiAlertConfig) -> None:
        """Set alert configuration"""

    @property
    def drift_type(self) -> DriftType:
        """Drift type"""

    @property
    def categorical_features(self) -> list[str]:
        """list of categorical features"""

    @categorical_features.setter
    def categorical_features(self, categorical_features: list[str]) -> None:
        """Set list of categorical features"""

    @staticmethod
    def load_from_json_file(path: Path) -> "PsiDriftConfig":
        """Load config from json file

        Args:
            path:
                Path to json file to load config from.
        """

    def __str__(self) -> str:
        """Return the string representation of the config."""

    def model_dump_json(self) -> str:
        """Return the json representation of the config."""

    def update_config_args(
        self,
        space: Optional[str] = None,
        name: Optional[str] = None,
        version: Optional[str] = None,
        alert_config: Optional[PsiAlertConfig] = None,
    ) -> None:
        """Inplace operation that updates config args

        Args:
            space:
                Model space
            name:
                Model name
            version:
                Model version
            alert_config:
                Alert configuration
        """

alert_config property writable

Alert configuration

categorical_features property writable

list of categorical features

drift_type property

Drift type

feature_map property

Feature map

name property writable

Model Name

space property writable

Model space

version property writable

Model version

__init__(space='__missing__', name='__missing__', version='0.1.0', alert_config=PsiAlertConfig(), config_path=None, categorical_features=None)

Initialize monitor config

Parameters:

Name	Type	Description	Default
space	str	Model space	'__missing__'
name	str	Model name	'__missing__'
version	str	Model version. Defaults to 0.1.0	'0.1.0'
alert_config	PsiAlertConfig	Alert configuration	PsiAlertConfig()
config_path	Optional[Path]	Optional path to load config from.	None
categorical_features	Optional[list[str]]	List of features to treat as categorical for PSI calculation.	None

Source code in python/scouter/drift/_drift.pyi

def __init__(
    self,
    space: str = "__missing__",
    name: str = "__missing__",
    version: str = "0.1.0",
    alert_config: PsiAlertConfig = PsiAlertConfig(),
    config_path: Optional[Path] = None,
    categorical_features: Optional[list[str]] = None,
):
    """Initialize monitor config

    Args:
        space:
            Model space
        name:
            Model name
        version:
            Model version. Defaults to 0.1.0
        alert_config:
            Alert configuration
        config_path:
            Optional path to load config from.
        categorical_features:
            List of features to treat as categorical for PSI calculation.
    """

__str__()

Return the string representation of the config.

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Return the string representation of the config."""

load_from_json_file(path) staticmethod

Load config from json file

Parameters:

Name	Type	Description	Default
path	Path	Path to json file to load config from.	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def load_from_json_file(path: Path) -> "PsiDriftConfig":
    """Load config from json file

    Args:
        path:
            Path to json file to load config from.
    """

model_dump_json()

Return the json representation of the config.

Source code in python/scouter/drift/_drift.pyi

def model_dump_json(self) -> str:
    """Return the json representation of the config."""

update_config_args(space=None, name=None, version=None, alert_config=None)

Inplace operation that updates config args

Parameters:

Name	Type	Description	Default
space	Optional[str]	Model space	None
name	Optional[str]	Model name	None
version	Optional[str]	Model version	None
alert_config	Optional[PsiAlertConfig]	Alert configuration	None

Source code in python/scouter/drift/_drift.pyi

def update_config_args(
    self,
    space: Optional[str] = None,
    name: Optional[str] = None,
    version: Optional[str] = None,
    alert_config: Optional[PsiAlertConfig] = None,
) -> None:
    """Inplace operation that updates config args

    Args:
        space:
            Model space
        name:
            Model name
        version:
            Model version
        alert_config:
            Alert configuration
    """

PsiDriftMap

Drift map of features

Source code in python/scouter/drift/_drift.pyi

class PsiDriftMap:
    """Drift map of features"""

    @property
    def space(self) -> str:
        """Space to associate with drift map"""

    @property
    def name(self) -> str:
        """name to associate with drift map"""

    @property
    def version(self) -> str:
        """Version to associate with drift map"""

    @property
    def features(self) -> Dict[str, float]:
        """Returns dictionary of features and their reported drift, if any"""

    def __str__(self) -> str:
        """Return string representation of data drift"""

    def model_dump_json(self) -> str:
        """Return json representation of data drift"""

    @staticmethod
    def model_validate_json(json_string: str) -> "PsiDriftMap":
        """Load drift map from json file.

        Args:
            json_string:
                JSON string representation of the drift map
        """

    def save_to_json(self, path: Optional[Path] = None) -> Path:
        """Save drift map to json file

        Args:
            path:
                Optional path to save the drift map. If None, outputs to `psi_drift_map.json`

        Returns:
            Path to the saved json file

        """

features property

Returns dictionary of features and their reported drift, if any

name property

name to associate with drift map

space property

Space to associate with drift map

version property

Version to associate with drift map

__str__()

Return string representation of data drift

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Return string representation of data drift"""

model_dump_json()

Return json representation of data drift

Source code in python/scouter/drift/_drift.pyi

def model_dump_json(self) -> str:
    """Return json representation of data drift"""

model_validate_json(json_string) staticmethod

Load drift map from json file.

Parameters:

Name	Type	Description	Default
json_string	str	JSON string representation of the drift map	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def model_validate_json(json_string: str) -> "PsiDriftMap":
    """Load drift map from json file.

    Args:
        json_string:
            JSON string representation of the drift map
    """

save_to_json(path=None)

Save drift map to json file

Parameters:

Name	Type	Description	Default
path	Optional[Path]	Optional path to save the drift map. If None, outputs to psi_drift_map.json	None

Returns:

Type	Description
Path	Path to the saved json file

Source code in python/scouter/drift/_drift.pyi

def save_to_json(self, path: Optional[Path] = None) -> Path:
    """Save drift map to json file

    Args:
        path:
            Optional path to save the drift map. If None, outputs to `psi_drift_map.json`

    Returns:
        Path to the saved json file

    """

PsiDriftProfile

Source code in python/scouter/drift/_drift.pyi

class PsiDriftProfile:
    @property
    def scouter_version(self) -> str:
        """Return scouter version used to create DriftProfile"""

    @property
    def features(self) -> Dict[str, PsiFeatureDriftProfile]:
        """Return the list of features."""

    @property
    def config(self) -> PsiDriftConfig:
        """Return the monitor config."""

    def model_dump_json(self) -> str:
        """Return json representation of drift profile"""

    def model_dump(self) -> Dict[str, Any]:
        """Return dictionary representation of drift profile"""

    def save_to_json(self, path: Optional[Path] = None) -> Path:
        """Save drift profile to json file

        Args:
            path:
                Optional path to save the drift profile. If None, outputs to `psi_drift_profile.json`

        Returns:
            Path to the saved json file
        """

    @staticmethod
    def model_validate_json(json_string: str) -> "PsiDriftProfile":
        """Load drift profile from json

        Args:
            json_string:
                JSON string representation of the drift profile

        """

    @staticmethod
    def from_file(path: Path) -> "PsiDriftProfile":
        """Load drift profile from file

        Args:
            path: Path to the file
        """

    @staticmethod
    def model_validate(data: Dict[str, Any]) -> "PsiDriftProfile":
        """Load drift profile from dictionary

        Args:
            data:
                DriftProfile dictionary
        """

    def update_config_args(
        self,
        space: Optional[str] = None,
        name: Optional[str] = None,
        version: Optional[str] = None,
        alert_config: Optional[PsiAlertConfig] = None,
    ) -> None:
        """Inplace operation that updates config args

        Args:
            name:
                Model name
            space:
                Model space
            version:
                Model version
            alert_config:
                Alert configuration
        """

    def __str__(self) -> str:
        """Sting representation of DriftProfile"""

config property

Return the monitor config.

features property

Return the list of features.

scouter_version property

Return scouter version used to create DriftProfile

__str__()

Sting representation of DriftProfile

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Sting representation of DriftProfile"""

from_file(path) staticmethod

Load drift profile from file

Parameters:

Name	Type	Description	Default
path	Path	Path to the file	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def from_file(path: Path) -> "PsiDriftProfile":
    """Load drift profile from file

    Args:
        path: Path to the file
    """

model_dump()

Return dictionary representation of drift profile

Source code in python/scouter/drift/_drift.pyi

def model_dump(self) -> Dict[str, Any]:
    """Return dictionary representation of drift profile"""

model_dump_json()

Return json representation of drift profile

Source code in python/scouter/drift/_drift.pyi

def model_dump_json(self) -> str:
    """Return json representation of drift profile"""

model_validate(data) staticmethod

Load drift profile from dictionary

Parameters:

Name	Type	Description	Default
data	Dict[str, Any]	DriftProfile dictionary	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def model_validate(data: Dict[str, Any]) -> "PsiDriftProfile":
    """Load drift profile from dictionary

    Args:
        data:
            DriftProfile dictionary
    """

model_validate_json(json_string) staticmethod

Load drift profile from json

Parameters:

Name	Type	Description	Default
json_string	str	JSON string representation of the drift profile	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def model_validate_json(json_string: str) -> "PsiDriftProfile":
    """Load drift profile from json

    Args:
        json_string:
            JSON string representation of the drift profile

    """

save_to_json(path=None)

Save drift profile to json file

Parameters:

Name	Type	Description	Default
path	Optional[Path]	Optional path to save the drift profile. If None, outputs to psi_drift_profile.json	None

Returns:

Type	Description
Path	Path to the saved json file

Source code in python/scouter/drift/_drift.pyi

def save_to_json(self, path: Optional[Path] = None) -> Path:
    """Save drift profile to json file

    Args:
        path:
            Optional path to save the drift profile. If None, outputs to `psi_drift_profile.json`

    Returns:
        Path to the saved json file
    """

update_config_args(space=None, name=None, version=None, alert_config=None)

Inplace operation that updates config args

Parameters:

Name	Type	Description	Default
name	Optional[str]	Model name	None
space	Optional[str]	Model space	None
version	Optional[str]	Model version	None
alert_config	Optional[PsiAlertConfig]	Alert configuration	None

Source code in python/scouter/drift/_drift.pyi

def update_config_args(
    self,
    space: Optional[str] = None,
    name: Optional[str] = None,
    version: Optional[str] = None,
    alert_config: Optional[PsiAlertConfig] = None,
) -> None:
    """Inplace operation that updates config args

    Args:
        name:
            Model name
        space:
            Model space
        version:
            Model version
        alert_config:
            Alert configuration
    """

PsiFeatureDriftProfile

Source code in python/scouter/drift/_drift.pyi

class PsiFeatureDriftProfile:
    @property
    def id(self) -> str:
        """Return the feature name"""

    @property
    def bins(self) -> List[Bin]:
        """Return the bins"""

    @property
    def timestamp(self) -> str:
        """Return the timestamp."""

    @property
    def bin_type(self) -> BinType:
        """Return the timestamp."""

bin_type property

Return the timestamp.

bins property

Return the bins

id property

Return the feature name

timestamp property

Return the timestamp.

SpcDriftConfig

Source code in python/scouter/drift/_drift.pyi

class SpcDriftConfig:
    def __init__(
        self,
        space: str = "__missing__",
        name: str = "__missing__",
        version: str = "0.1.0",
        sample_size: int = 25,
        alert_config: SpcAlertConfig = SpcAlertConfig(),
        config_path: Optional[Path] = None,
    ):
        """Initialize monitor config

        Args:
            space:
                Model space
            name:
                Model name
            version:
                Model version. Defaults to 0.1.0
            sample_size:
                Sample size
            alert_config:
                Alert configuration
            config_path:
                Optional path to load config from.
        """

    @property
    def sample_size(self) -> int:
        """Return the sample size."""

    @sample_size.setter
    def sample_size(self, sample_size: int) -> None:
        """Set the sample size."""

    @property
    def name(self) -> str:
        """Model Name"""

    @name.setter
    def name(self, name: str) -> None:
        """Set model name"""

    @property
    def space(self) -> str:
        """Model space"""

    @space.setter
    def space(self, space: str) -> None:
        """Set model space"""

    @property
    def version(self) -> str:
        """Model version"""

    @version.setter
    def version(self, version: str) -> None:
        """Set model version"""

    @property
    def feature_map(self) -> Optional[FeatureMap]:
        """Feature map"""

    @property
    def alert_config(self) -> SpcAlertConfig:
        """Alert configuration"""

    @alert_config.setter
    def alert_config(self, alert_config: SpcAlertConfig) -> None:
        """Set alert configuration"""

    @property
    def drift_type(self) -> DriftType:
        """Drift type"""

    @staticmethod
    def load_from_json_file(path: Path) -> "SpcDriftConfig":
        """Load config from json file

        Args:
            path:
                Path to json file to load config from.
        """

    def __str__(self) -> str:
        """Return the string representation of the config."""

    def model_dump_json(self) -> str:
        """Return the json representation of the config."""

    def update_config_args(
        self,
        space: Optional[str] = None,
        name: Optional[str] = None,
        version: Optional[str] = None,
        sample_size: Optional[int] = None,
        alert_config: Optional[SpcAlertConfig] = None,
    ) -> None:
        """Inplace operation that updates config args

        Args:
            space:
                Model space
            name:
                Model name
            version:
                Model version
            sample_size:
                Sample size
            alert_config:
                Alert configuration
        """

alert_config property writable

Alert configuration

drift_type property

Drift type

feature_map property

Feature map

name property writable

Model Name

sample_size property writable

Return the sample size.

space property writable

Model space

version property writable

Model version

__init__(space='__missing__', name='__missing__', version='0.1.0', sample_size=25, alert_config=SpcAlertConfig(), config_path=None)

Initialize monitor config

Parameters:

Name	Type	Description	Default
space	str	Model space	'__missing__'
name	str	Model name	'__missing__'
version	str	Model version. Defaults to 0.1.0	'0.1.0'
sample_size	int	Sample size	25
alert_config	SpcAlertConfig	Alert configuration	SpcAlertConfig()
config_path	Optional[Path]	Optional path to load config from.	None

Source code in python/scouter/drift/_drift.pyi

def __init__(
    self,
    space: str = "__missing__",
    name: str = "__missing__",
    version: str = "0.1.0",
    sample_size: int = 25,
    alert_config: SpcAlertConfig = SpcAlertConfig(),
    config_path: Optional[Path] = None,
):
    """Initialize monitor config

    Args:
        space:
            Model space
        name:
            Model name
        version:
            Model version. Defaults to 0.1.0
        sample_size:
            Sample size
        alert_config:
            Alert configuration
        config_path:
            Optional path to load config from.
    """

__str__()

Return the string representation of the config.

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Return the string representation of the config."""

load_from_json_file(path) staticmethod

Load config from json file

Parameters:

Name	Type	Description	Default
path	Path	Path to json file to load config from.	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def load_from_json_file(path: Path) -> "SpcDriftConfig":
    """Load config from json file

    Args:
        path:
            Path to json file to load config from.
    """

model_dump_json()

Return the json representation of the config.

Source code in python/scouter/drift/_drift.pyi

def model_dump_json(self) -> str:
    """Return the json representation of the config."""

update_config_args(space=None, name=None, version=None, sample_size=None, alert_config=None)

Inplace operation that updates config args

Parameters:

Name	Type	Description	Default
space	Optional[str]	Model space	None
name	Optional[str]	Model name	None
version	Optional[str]	Model version	None
sample_size	Optional[int]	Sample size	None
alert_config	Optional[SpcAlertConfig]	Alert configuration	None

Source code in python/scouter/drift/_drift.pyi

def update_config_args(
    self,
    space: Optional[str] = None,
    name: Optional[str] = None,
    version: Optional[str] = None,
    sample_size: Optional[int] = None,
    alert_config: Optional[SpcAlertConfig] = None,
) -> None:
    """Inplace operation that updates config args

    Args:
        space:
            Model space
        name:
            Model name
        version:
            Model version
        sample_size:
            Sample size
        alert_config:
            Alert configuration
    """

SpcDriftMap

Drift map of features

Source code in python/scouter/drift/_drift.pyi

class SpcDriftMap:
    """Drift map of features"""

    @property
    def space(self) -> str:
        """Space to associate with drift map"""

    @property
    def name(self) -> str:
        """name to associate with drift map"""

    @property
    def version(self) -> str:
        """Version to associate with drift map"""

    @property
    def features(self) -> Dict[str, SpcFeatureDrift]:
        """Returns dictionary of features and their data profiles"""

    def __str__(self) -> str:
        """Return string representation of data drift"""

    def model_dump_json(self) -> str:
        """Return json representation of data drift"""

    @staticmethod
    def model_validate_json(json_string: str) -> "SpcDriftMap":
        """Load drift map from json file.

        Args:
            json_string:
                JSON string representation of the drift map
        """

    def save_to_json(self, path: Optional[Path] = None) -> Path:
        """Save drift map to json file

        Args:
            path:
                Optional path to save the drift map. If None, outputs to `spc_drift_map.json`

        Returns:
            Path to the saved json file

        """

    def to_numpy(self) -> Any:
        """Return drift map as a tuple of sample_array, drift_array and list of features"""

features property

Returns dictionary of features and their data profiles

name property

name to associate with drift map

space property

Space to associate with drift map

version property

Version to associate with drift map

__str__()

Return string representation of data drift

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Return string representation of data drift"""

model_dump_json()

Return json representation of data drift

Source code in python/scouter/drift/_drift.pyi

def model_dump_json(self) -> str:
    """Return json representation of data drift"""

model_validate_json(json_string) staticmethod

Load drift map from json file.

Parameters:

Name	Type	Description	Default
json_string	str	JSON string representation of the drift map	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def model_validate_json(json_string: str) -> "SpcDriftMap":
    """Load drift map from json file.

    Args:
        json_string:
            JSON string representation of the drift map
    """

save_to_json(path=None)

Save drift map to json file

Parameters:

Name	Type	Description	Default
path	Optional[Path]	Optional path to save the drift map. If None, outputs to spc_drift_map.json	None

Returns:

Type	Description
Path	Path to the saved json file

Source code in python/scouter/drift/_drift.pyi

def save_to_json(self, path: Optional[Path] = None) -> Path:
    """Save drift map to json file

    Args:
        path:
            Optional path to save the drift map. If None, outputs to `spc_drift_map.json`

    Returns:
        Path to the saved json file

    """

to_numpy()

Return drift map as a tuple of sample_array, drift_array and list of features

Source code in python/scouter/drift/_drift.pyi

def to_numpy(self) -> Any:
    """Return drift map as a tuple of sample_array, drift_array and list of features"""

SpcDriftProfile

Source code in python/scouter/drift/_drift.pyi

class SpcDriftProfile:
    @property
    def scouter_version(self) -> str:
        """Return scouter version used to create DriftProfile"""

    @property
    def features(self) -> Dict[str, SpcFeatureDriftProfile]:
        """Return the list of features."""

    @property
    def config(self) -> SpcDriftConfig:
        """Return the monitor config."""

    def model_dump_json(self) -> str:
        """Return json representation of drift profile"""

    def model_dump(self) -> Dict[str, Any]:
        """Return dictionary representation of drift profile"""

    def save_to_json(self, path: Optional[Path] = None) -> Path:
        """Save drift profile to json file

        Args:
            path:
                Optional path to save the drift profile. If None, outputs to `spc_drift_profile.json`


        Returns:
            Path to the saved json file
        """

    @staticmethod
    def model_validate_json(json_string: str) -> "SpcDriftProfile":
        """Load drift profile from json

        Args:
            json_string:
                JSON string representation of the drift profile

        """

    @staticmethod
    def from_file(path: Path) -> "SpcDriftProfile":
        """Load drift profile from file

        Args:
            path: Path to the file
        """

    @staticmethod
    def model_validate(data: Dict[str, Any]) -> "SpcDriftProfile":
        """Load drift profile from dictionary

        Args:
            data:
                DriftProfile dictionary
        """

    def update_config_args(
        self,
        space: Optional[str] = None,
        name: Optional[str] = None,
        version: Optional[str] = None,
        sample_size: Optional[int] = None,
        alert_config: Optional[SpcAlertConfig] = None,
    ) -> None:
        """Inplace operation that updates config args

        Args:
            name:
                Model name
            space:
                Model space
            version:
                Model version
            sample_size:
                Sample size
            alert_config:
                Alert configuration
        """

    def __str__(self) -> str:
        """Sting representation of DriftProfile"""

config property

Return the monitor config.

features property

Return the list of features.

scouter_version property

Return scouter version used to create DriftProfile

__str__()

Sting representation of DriftProfile

Source code in python/scouter/drift/_drift.pyi

def __str__(self) -> str:
    """Sting representation of DriftProfile"""

from_file(path) staticmethod

Load drift profile from file

Parameters:

Name	Type	Description	Default
path	Path	Path to the file	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def from_file(path: Path) -> "SpcDriftProfile":
    """Load drift profile from file

    Args:
        path: Path to the file
    """

model_dump()

Return dictionary representation of drift profile

Source code in python/scouter/drift/_drift.pyi

def model_dump(self) -> Dict[str, Any]:
    """Return dictionary representation of drift profile"""

model_dump_json()

Return json representation of drift profile

Source code in python/scouter/drift/_drift.pyi

def model_dump_json(self) -> str:
    """Return json representation of drift profile"""

model_validate(data) staticmethod

Load drift profile from dictionary

Parameters:

Name	Type	Description	Default
data	Dict[str, Any]	DriftProfile dictionary	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def model_validate(data: Dict[str, Any]) -> "SpcDriftProfile":
    """Load drift profile from dictionary

    Args:
        data:
            DriftProfile dictionary
    """

model_validate_json(json_string) staticmethod

Load drift profile from json

Parameters:

Name	Type	Description	Default
json_string	str	JSON string representation of the drift profile	required

Source code in python/scouter/drift/_drift.pyi

@staticmethod
def model_validate_json(json_string: str) -> "SpcDriftProfile":
    """Load drift profile from json

    Args:
        json_string:
            JSON string representation of the drift profile

    """

save_to_json(path=None)

Save drift profile to json file

Parameters:

Name	Type	Description	Default
path	Optional[Path]	Optional path to save the drift profile. If None, outputs to spc_drift_profile.json	None

Returns:

Type	Description
Path	Path to the saved json file

Source code in python/scouter/drift/_drift.pyi

def save_to_json(self, path: Optional[Path] = None) -> Path:
    """Save drift profile to json file

    Args:
        path:
            Optional path to save the drift profile. If None, outputs to `spc_drift_profile.json`


    Returns:
        Path to the saved json file
    """

update_config_args(space=None, name=None, version=None, sample_size=None, alert_config=None)

Inplace operation that updates config args

Parameters:

Name	Type	Description	Default
name	Optional[str]	Model name	None
space	Optional[str]	Model space	None
version	Optional[str]	Model version	None
sample_size	Optional[int]	Sample size	None
alert_config	Optional[SpcAlertConfig]	Alert configuration	None

Source code in python/scouter/drift/_drift.pyi

def update_config_args(
    self,
    space: Optional[str] = None,
    name: Optional[str] = None,
    version: Optional[str] = None,
    sample_size: Optional[int] = None,
    alert_config: Optional[SpcAlertConfig] = None,
) -> None:
    """Inplace operation that updates config args

    Args:
        name:
            Model name
        space:
            Model space
        version:
            Model version
        sample_size:
            Sample size
        alert_config:
            Alert configuration
    """

SpcFeatureDrift

Source code in python/scouter/drift/_drift.pyi

class SpcFeatureDrift:
    @property
    def samples(self) -> List[float]:
        """Return list of samples"""

    @property
    def drift(self) -> List[float]:
        """Return list of drift values"""

drift property

Return list of drift values

samples property

Return list of samples

SpcFeatureDriftProfile

Source code in python/scouter/drift/_drift.pyi

class SpcFeatureDriftProfile:
    @property
    def id(self) -> str:
        """Return the id."""

    @property
    def center(self) -> float:
        """Return the center."""

    @property
    def one_ucl(self) -> float:
        """Return the zone 1 ucl."""

    @property
    def one_lcl(self) -> float:
        """Return the zone 1 lcl."""

    @property
    def two_ucl(self) -> float:
        """Return the zone 2 ucl."""

    @property
    def two_lcl(self) -> float:
        """Return the zone 2 lcl."""

    @property
    def three_ucl(self) -> float:
        """Return the zone 3 ucl."""

    @property
    def three_lcl(self) -> float:
        """Return the zone 3 lcl."""

    @property
    def timestamp(self) -> str:
        """Return the timestamp."""

center property

Return the center.

id property

Return the id.

one_lcl property

Return the zone 1 lcl.

one_ucl property

Return the zone 1 ucl.

three_lcl property

Return the zone 3 lcl.

three_ucl property

Return the zone 3 ucl.

timestamp property

Return the timestamp.

two_lcl property

Return the zone 2 lcl.

two_ucl property

Return the zone 2 ucl.

Workflow

Bases: Protocol

Potato Head Workflow Protocol

Source code in python/scouter/drift/_drift.pyi

class Workflow(Protocol):
    """Potato Head Workflow Protocol"""