from typing import Any, Dict, List
from aeon.classification.dictionary_based import ContractableBOSS
from ml_grid.pipeline.data import pipe
[docs]
class ContractableBOSS_class:
"""A wrapper for the aeon ContractableBOSS time-series classifier.
This class provides a consistent interface for the ContractableBOSS
classifier, including defining a hyperparameter search space.
Attributes:
algorithm_implementation: An instance of the aeon ContractableBOSS
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: ContractableBOSS
[docs]
parameter_space: Dict[str, List[Any]]
def __init__(self, ml_grid_object: pipe):
"""Initializes the ContractableBOSS_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
n_jobs_model_val = ml_grid_object.global_params.n_jobs_model_val
random_state_val = ml_grid_object.global_params.random_state_val
self.algorithm_implementation = ContractableBOSS()
self.method_name = "ContractableBOSS"
self.parameter_space = {
"n_parameter_samples": [100, 250, 500], # Number of parameter combos to try
"max_ensemble_size": [
30,
50,
100,
], # Maximum number of classifiers to retain
"max_win_len_prop": [
0.8,
1.0,
], # Maximum window length as a proportion of series length
"min_window": [5, 10, 15], # Minimum window size
"time_limit_in_minutes": time_limit_param, # Time contract to limit build time in minutes
"contract_max_n_parameter_samples": [
1000,
2000,
], # Max number of parameter combos when time_limit_in_minutes is set
"save_train_predictions": [
True,
False,
], # Save ensemble member train predictions in fit for LOOCV
"n_jobs": [
n_jobs_model_val
], # Number of jobs to run in parallel for fit and predict
"feature_selection": [
"chi2",
"none",
"random",
], # Sets the feature selection strategy to be used
"random_state": [random_state_val], # Seed for random integer
}