BaseTpcpObject#

class tpcp.BaseTpcpObject[source]#

Baseclass for all tpcp objects.

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.

__init__(*args, **kwargs)#
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=.

Examples using tpcp.BaseTpcpObject#

Custom Dataset - Basics

Custom Dataset - Basics

Custom Dataset - A real world example

Custom Dataset - A real world example

The final ECG Example dataset

The final ECG Example dataset

Algorithms - A real world example: QRS-Detection

Algorithms - A real world example: QRS-Detection

The final QRS detection algorithms

The final QRS detection algorithms

Grid Search optimal Algorithm Parameter

Grid Search optimal Algorithm Parameter

Optimizable Pipelines

Optimizable Pipelines

GridSearchCV

GridSearchCV

Custom Optuna Optimizer

Custom Optuna Optimizer

Build-in Optuna Optimizers

Build-in Optuna Optimizers

Validation

Validation

Cross Validation

Cross Validation

Custom Scorer

Custom Scorer

Tensorflow/Keras

Tensorflow/Keras

Caching

Caching

Dataclass and Attrs support

Dataclass and Attrs support

Composite-Algorithms and Pipelines

Composite-Algorithms and Pipelines

TypedIterator

TypedIterator

Optimization Info

Optimization Info