from typing import Any, Dict, List
from aeon.classification.dictionary_based._tde import IndividualTDE
from skopt.space import Categorical, Integer
from ml_grid.pipeline.data import pipe
[docs]
class IndividualTDE_class:
"""A wrapper for the aeon IndividualTDE time-series classifier.
This class provides a consistent interface for the IndividualTDE classifier,
including defining a hyperparameter search space.
Attributes:
algorithm_implementation: An instance of the aeon IndividualTDE
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: IndividualTDE
[docs]
parameter_space: Dict[str, List[Any]]
def __init__(self, ml_grid_object: pipe):
"""Initializes the IndividualTDE_class.
Args:
ml_grid_object (pipe): An instance of the main data pipeline object.
"""
random_state_val = ml_grid_object.global_params.random_state_val
n_jobs_model_val = ml_grid_object.global_params.n_jobs_model_val
self.algorithm_implementation: IndividualTDE = IndividualTDE()
self.method_name: str = "IndividualTDE"
if getattr(ml_grid_object.global_params, "test_mode", False):
self.parameter_space = {
"window_size": [10],
"word_length": [8],
"n_jobs": [1],
}
return
if ml_grid_object.global_params.bayessearch:
self.parameter_space = {
"window_size": Integer(5, 15),
"word_length": Integer(4, 12),
"norm": Categorical([True, False]),
"levels": Integer(1, 3),
"igb": Categorical([True, False]),
"alphabet_size": Integer(3, 5),
"bigrams": Categorical([True, False]),
"dim_threshold": Categorical([0.8, 0.85, 0.9]),
"max_dims": Integer(15, 25),
"typed_dict": Categorical([True, False]),
"n_jobs": [n_jobs_model_val],
"random_state": [random_state_val],
}
else:
self.parameter_space = {
"window_size": [5, 10, 15],
"word_length": [4, 8, 12],
"norm": [True, False],
"levels": [1, 2, 3],
"igb": [True, False],
"alphabet_size": [3, 4, 5],
"bigrams": [True, False],
"dim_threshold": [0.8, 0.85, 0.9],
"max_dims": [15, 20, 25],
"typed_dict": [True, False],
"n_jobs": [n_jobs_model_val],
"random_state": [random_state_val],
}