geostep.designer.SimpleRandomizationDesigner

class geostep.designer.SimpleRandomizationDesigner(num_groups: int = 2, seed: int = 42)[source]

Methods

`__init__`([num_groups, seed])	Initialize designer with configuration.
`design`(df, geo_col)	Assigns geographic units to experimental groups using simple randomization.
`enable_monitoring`([enabled])	Enable or disable performance monitoring.
`get_metrics`()	Get performance and execution metrics.
`monitor_operation`(operation_name[, ...])	Context manager for monitoring operations.
`post_process_design`(design_df)	Post-process design results.
`prepare_data`([df, geo_col])	Prepare data for simple randomization.
`set_metrics_collector`(collector)	Set the metrics collector for this instance.
`validate_inputs`([df, geo_col])	Validate inputs for simple randomization design.

Attributes

__init__(num_groups: int = 2, seed: int = 42)[source]: Initialize designer with configuration.

validate_inputs(df: DataFrame = None, geo_col: str = None, **kwargs) → None[source]

Validate inputs for simple randomization design.

Parameters:

df (pd.DataFrame) – DataFrame containing the geographic units to randomize.
geo_col (str) – The name of the column containing the unique geographic identifiers.

Raises:

prepare_data(df: DataFrame = None, geo_col: str = None, **kwargs) → None[source]

Prepare data for simple randomization.

Parameters:

df (pd.DataFrame) – DataFrame containing the geographic units to randomize.
geo_col (str) – The name of the column containing the unique geographic identifiers.

design(df: DataFrame, geo_col: str) → DataFrame[source]

Assigns geographic units to experimental groups using simple randomization.

Parameters:

df (pd.DataFrame) – DataFrame containing the geographic units to randomize.
geo_col (str) – The name of the column containing the unique geographic identifiers.

Returns:

A DataFrame with a new ‘assignment’ column.

Return type:

pd.DataFrame

Raises:

ValidationError – If input validation fails.