tpcp.misc.BaseTypedIterator#

class tpcp.misc.BaseTypedIterator(data_type: type[DataclassT], aggregations: Sequence[tuple[str, Callable[[list, list], Any]]] = cf([]))[source]#

A Base class to implement custom typed iterators.

This class is missing the iterate method, which needs to be implemented by the child class. It has a _iterate method though that does most of the heavy lifting. The actual iterate method should handle turning the inputs into an iterable and then call _iterate.

TypedIterator provides a “dummy” implementation that expects any type of iterator for the iterate method. Custom base classes could provide more elaborate preprocessing of the inputs before iteration. For example, cutting sections out of a dataframe based on a list of start and end indices, and then iterating over the cut sections.

Parameters:

data_type: A dataclass that defines the result type you expect from each iteration.
aggregations: An optional list of aggregations to apply to the results. This has the form [(result_name, aggregation_function), ...]. If a result-name is in the list, the aggregation will be applied to it, when accessing the respective result attribute (i.e. {result_name}_). If no aggregation is defined for a result, a simple list of all results will be returned.
NULL_VALUE: (Class attribute) The value that is used to initialize the result dataclass and will remain in the results, if no result was for a specific attribute in one or more iterations.

Attributes:

inputs_: List of all input elements that were iterated over.
raw_results_: List of all results as dataclass instances. The attribute of the dataclass instance will have the a value of _NOT_SET if no result was set. To check for this, you can use isinstance(val, TypedIterator.NULL_VALUE).
{result_name}_: The aggregated results for the respective result name.
done_: True, if the iterator is done. If the iterator is not done, but you try to access the results, a warning will be raised.

Methods

`clone`()	Create a new instance of the class with all parameters copied over.
`get_params`([deep])	Get parameters for this algorithm.
`set_params`(**params)	Set the parameters of this Algorithm.

clone() → Self[source]#

Create a new instance of the class with all parameters copied over.

This will create a new instance of the class itself and all nested objects

get_params(deep: bool = True) → dict[str, Any][source]#

Get parameters for this algorithm.

Parameters:

deep: Only relevant if object contains nested algorithm objects. If this is the case and deep is True, the params of these nested objects are included in the output using a prefix like nested_object_name__ (Note the two “_” at the end)

Returns:

params: Parameter names mapped to their values.

set_params(**params: Any) → Self[source]#

Set the parameters of this Algorithm.

To set parameters of nested objects use nested_object_name__para_name=.