from typing import Any, Dict, List
from aeon.classification.distance_based import ElasticEnsemble
from skopt.space import Categorical, Real
from ml_grid.pipeline.data import pipe
[docs]
class ElasticEnsemble_class:
"""A wrapper for the aeon ElasticEnsemble time-series classifier.
This class provides a consistent interface for the ElasticEnsemble
classifier, including defining a hyperparameter search space.
Attributes:
algorithm_implementation: An instance of the aeon ElasticEnsemble
classifier.
method_name (str): The name of the classifier method.
parameter_space (Dict[str, List[Any]]): The hyperparameter search space
for the classifier.
"""
[docs]
algorithm_implementation: ElasticEnsemble
[docs]
parameter_space: Dict[str, List[Any]]
def __init__(self, ml_grid_object: pipe):
"""Initializes the ElasticEnsemble_class.
Args:
ml_grid_object (pipe): An instance of the main data pipeline object.
"""
n_jobs_model_val = ml_grid_object.global_params.n_jobs_model_val
self.algorithm_implementation = ElasticEnsemble()
self.method_name = "ElasticEnsemble"
if getattr(ml_grid_object.global_params, "test_mode", False):
self.parameter_space = {
"proportion_of_param_options": [0.1],
"proportion_train_in_param_finding": [0.1],
"proportion_train_for_test": [0.1],
"n_jobs": [1],
}
return
if ml_grid_object.global_params.bayessearch:
self.parameter_space = {
"proportion_of_param_options": Real(0.6, 0.8),
"proportion_train_in_param_finding": Real(0.6, 0.8),
"proportion_train_for_test": Real(0.6, 0.8),
"n_jobs": [n_jobs_model_val],
"majority_vote": Categorical([False, True]),
}
else:
self.parameter_space = {
"proportion_of_param_options": [0.8, 0.7, 0.6],
"proportion_train_in_param_finding": [0.8, 0.7, 0.6],
"proportion_train_for_test": [0.8, 0.7, 0.6],
"n_jobs": [n_jobs_model_val],
"majority_vote": [False, True],
}