from typing import Any, Dict, List
from aeon.classification.convolution_based import RocketClassifier
from skopt.space import Categorical
from ml_grid.pipeline.data import pipe
[docs]
class RocketClassifier_class:
"""A wrapper for the aeon RocketClassifier time-series classifier.
This class provides a consistent interface for the RocketClassifier,
including defining a hyperparameter search space.
Attributes:
algorithm_implementation: An instance of the aeon RocketClassifier.
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: RocketClassifier
[docs]
parameter_space: Dict[str, List[Any]]
def __init__(self, ml_grid_object: pipe):
"""Initializes the RocketClassifier_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 = RocketClassifier()
self.method_name = "RocketClassifier"
gp = ml_grid_object.global_params
test_mode = getattr(gp, "test_mode", False)
if not test_mode and hasattr(gp, "__dict__"):
test_mode = gp.__dict__.get("test_mode", False)
if test_mode:
self.parameter_space = {
"n_kernels": [100],
"random_state": [random_state_val],
"estimator": [None],
"n_jobs": [1],
}
elif ml_grid_object.global_params.bayessearch:
self.parameter_space = {
"n_kernels": Categorical([5000, 10000, 15000]),
"random_state": [random_state_val],
"estimator": [None],
"n_jobs": [n_jobs_model_val],
}
else:
self.parameter_space = {
"n_kernels": [5000, 10000, 15000],
"random_state": [random_state_val],
"estimator": [None],
"n_jobs": [n_jobs_model_val],
}