from typing import Any, Dict, List
from aeon.classification.hybrid._hivecote_v2 import HIVECOTEV2
from ml_grid.pipeline.data import pipe
[docs]
class HIVECOTEV2_class:
"""A wrapper for the aeon HIVECOTEV2 time-series classifier.
This class provides a consistent interface for the HIVECOTEV2 classifier,
including defining a hyperparameter search space.
Attributes:
algorithm_implementation: An instance of the aeon HIVECOTEV2 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: HIVECOTEV2
[docs]
parameter_space: Dict[str, List[Any]]
def __init__(self, ml_grid_object: pipe):
"""Initializes the HIVECOTEV2_class.
Args:
ml_grid_object (pipe): An instance of the main data pipeline object.
"""
time_limit_param = ml_grid_object.global_params.time_limit_param
if isinstance(time_limit_param, list):
time_limit_param = time_limit_param[0]
random_state_val = ml_grid_object.global_params.random_state_val
verbose_param = ml_grid_object.verbose
n_jobs_model_val = ml_grid_object.global_params.n_jobs_model_val
self.algorithm_implementation = HIVECOTEV2()
self.method_name = "HIVECOTEV2"
if getattr(ml_grid_object.global_params, "test_mode", False):
self.parameter_space = {
"time_limit_in_minutes": [0.05],
"verbose": [0],
"n_jobs": [1],
}
return
if ml_grid_object.global_params.bayessearch:
self.parameter_space = {
"stc_params": [None],
"drcif_params": [None],
"arsenal_params": [None],
"tde_params": [None],
"time_limit_in_minutes": time_limit_param,
"save_component_probas": [False],
"verbose": [verbose_param],
"random_state": [random_state_val],
"n_jobs": [n_jobs_model_val],
}
else:
self.parameter_space = {
"stc_params": [
None
], # Parameters for the ShapeletTransformClassifier module. If None, uses the default parameters with a 2-hour transform contract.
"drcif_params": [
None
], # Parameters for the DrCIF module. If None, uses the default parameters with n_estimators set to 500.
"arsenal_params": [
None
], # Parameters for the Arsenal module. If None, uses the default parameters.
"tde_params": [
None
], # Parameters for the TemporalDictionaryEnsemble module. If None, uses the default parameters.
"time_limit_in_minutes": time_limit_param, # Time contract to limit build time in minutes, overriding n_estimators/n_parameter_samples for each component. Default of 0 means n_estimators/n_parameter_samples for each component is used.
"save_component_probas": [
False
], # When predict/predict_proba is called, save each HIVE-COTEV2 component probability predictions in component_probas.
"verbose": [verbose_param],
"random_state": [
random_state_val
], # If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
"n_jobs": [
n_jobs_model_val
], # The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
#'parallel_backend': [ 'multiprocessing'], #None, 'loky', , 'threading'# Specify the parallelization backend implementation in joblib for Catch22, if None a ‘prefer’ value of “threads” is used by default. Valid options are “loky”, “multiprocessing”, “threading” or a custom backend. See the joblib Parallel documentation for more details.
}