model

Model

Operations for working with machine learning models.

add_analysis_data classmethod

add_analysis_data(model_id: str, data: pd.DataFrame) -> None

Add analysis data to a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model.	required
data	DataFrame	Data to be added.	required

Note

This method does not update existing data. It only adds new data. If you want to update existing data, use upsert_analysis_data instead.

add_analysis_target_data classmethod

add_analysis_target_data(model_id: str, data: pd.DataFrame) -> None

Add (delayed) target data to a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model.	required
data	DataFrame	Data to be added.	required

Note

This method can only be used if the model has a target data source. If you want to add analysis data to a model without a target data source, use add_analysis_data instead.

Note

This method does not update existing data. It only adds new data. If you want to update existing data, use upsert_analysis_target_data instead.

add_custom_metric classmethod

add_custom_metric(model_id: str, metric_id: str) -> None

Add a custom metric to a monitoring model.

create classmethod

create(name: str, schema: ModelSchema, reference_data: pd.DataFrame, analysis_data: pd.DataFrame, key_performance_metric: PerformanceMetric, key_performance_metric_component: Optional[str] = None, target_data: Optional[pd.DataFrame] = None, chunk_period: Optional[ChunkPeriod] = None, chunk_size: Optional[int] = None) -> ModelDetails

Create a new model.

Parameters:

Name	Type	Description	Default
name	str	Name for the model.	required
schema	ModelSchema	Schema of the model. Typically, created using Schema.from_df.	required
reference_data	DataFrame	Reference data to use for the model.	required
analysis_data	DataFrame	Analysis data to use for the model. If the data contains targets, targets must always be provided together with analysis data.	required
key_performance_metric	PerformanceMetric	Key performance metric for the model.	required
key_performance_metric_component	Optional[str]	Optional key performance metric component for the model. This is only required if the key performance metric is a composite metric, for example the CONFUSION_MATRIX metric that has components like true_positives, false_positives, etc.	None
target_data	Optional[DataFrame]	Optional target data to use for the model.	None
chunk_period	Optional[ChunkPeriod]	Time period per chunk. Should only be set for time-based chunking.	None
chunk_size	Optional[int]	Number of rows per chunk. Should only be set for size-based chunking.	None

Returns:

Type	Description
ModelDetails	Detailed about the model once it has been created.

delete classmethod

delete(model_id: str) -> None

Delete a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model to delete.	required

delete_analysis_data classmethod

delete_analysis_data(model_id: str, data_ids: pd.DataFrame) -> None

Delete analysis data from a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model.	required
data_ids	DataFrame	ID's for the data to be deleted.	required

delete_analysis_target_data classmethod

delete_analysis_target_data(model_id: str, data_ids: pd.DataFrame) -> None

Delete target data from a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model.	required
data_ids	DataFrame	ID's for the data to be deleted.	required

get classmethod

get(model_id: str) -> ModelDetails

Get details for a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model to get details for.	required

Returns:

Type	Description
ModelDetails	Detailed information about the model.

get_analysis_data_history classmethod

get_analysis_data_history(model_id: str) -> List[DataSourceEvent]

Get analysis data history for a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model.	required

Returns:

Type	Description
List[DataSourceEvent]	List of events related to analysis data for the model.

get_analysis_target_data_history classmethod

get_analysis_target_data_history(model_id: str) -> List[DataSourceEvent]

Get target data history for a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model.	required

Returns:

Type	Description
List[DataSourceEvent]	List of events related to target data for the model.

get_reference_data_history classmethod

get_reference_data_history(model_id: str) -> List[DataSourceEvent]

Get reference data history for a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model.	required

Returns:

Type	Description
List[DataSourceEvent]	List of events related to reference data for the model.

list classmethod

list(name: Optional[str] = None, problem_type: Optional[ProblemType] = None) -> List[ModelSummary]

List defined models.

Parameters:

Name	Type	Description	Default
name	Optional[str]	Optional name filter.	None
problem_type	Optional[ProblemType]	Optional problem type filter.	None

Returns:

Type	Description
List[ModelSummary]	List of models that match the provided filter criteria.

remove_custom_metric classmethod

remove_custom_metric(model_id: str, metric_id: str) -> None

Remove a custom metric from a monitoring model.

upsert_analysis_data classmethod

upsert_analysis_data(model_id: str, data: pd.DataFrame) -> None

Add or update analysis data for a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model.	required
data	DataFrame	Data to be added/updated.	required

Note

This method compares existing data with the new data to determine which rows to update and which to add. If you are certain you are only adding new data, it is recommended to use add_analysis_data instead for better performance.

upsert_analysis_target_data classmethod

upsert_analysis_target_data(model_id: str, data: pd.DataFrame) -> None

Add or update (delayed) target data for a model.

Parameters:

Name	Type	Description	Default
model_id	str	ID of the model.	required
data	DataFrame	Data to be added/updated.	required

Note

This method can only be used if the model has a target data source. If you want to update analysis data in a model without a target data source, use upsert_analysis_data instead.

Note

This method compares existing data with the new data to determine which rows to update and which to add. If you are certain you are only adding new data, it is recommended to use add_analysis_target_data instead for better performance.

ModelDetails

Bases: ModelSummary

Detailed information about a model.

Attributes:

Name	Type	Description
latestRun	Optional[RunSummary]	The currently active run or latest run performed for the model. This is None if no runs have been performed yet.
nextRun	Optional[RunSummary]	The next run scheduled for the model. This is None if there is a run currently active.

ModelSummary

Bases: TypedDict

Summary of a model.

Attributes:

Name	Type	Description
id	str	Unique identifier of the model (generated by NannyML Cloud when a model is created).
name	str	User-defined name of the model.
problemType	ProblemType	Type of problem the model is trying to solve.
createdAt	datetime	Timestamp when the model was created.