Source code for OrdinalTDEClassifier_module

from typing import Any, Dict, List

from aeon.classification.ordinal_classification import OrdinalTDE
from ml_grid.pipeline.data import pipe


# unknown ts
[docs] class OrdinalTDE_class: """A wrapper for the aeon OrdinalTDE time-series classifier.""" def __init__(self, ml_grid_object: pipe): """Initializes the OrdinalTDE_class. Args: ml_grid_object (pipe): The main data pipeline object, which contains data and global parameters. """ random_state_val = ml_grid_object.global_params.random_state_val n_jobs_model_val = ml_grid_object.global_params.n_jobs_model_val time_limit_param = ml_grid_object.global_params.time_limit_param
[docs] self.algorithm_implementation: OrdinalTDE = OrdinalTDE()
[docs] self.method_name: str = "OrdinalTDE"
[docs] self.parameter_space: Dict[str, List[Any]] = { "n_parameter_samples": [ 100, 250, 500, ], # Number of parameter combinations to consider for the final ensemble "max_ensemble_size": [ 30, 50, 100, ], # Maximum number of estimators in the ensemble "max_win_len_prop": [ 0.8, 1.0, ], # Maximum window length as a proportion of series length "min_window": [5, 10, 15], # Minimum window length "randomly_selected_params": [ 30, 50, 70, ], # Number of parameters randomly selected before Gaussian process parameter selection "bigrams": [True, False, None], # Whether to use bigrams "dim_threshold": [ 0.75, 0.85, 0.95, ], # Dimension accuracy threshold for multivariate data "max_dims": [ 10, 20, 30, ], # Max number of dimensions per classifier for multivariate data "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 combinations when time_limit_in_minutes is set "typed_dict": [ True, False, ], # Whether to use numba typed Dict to store word counts #'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 "random_state": [random_state_val], # Seed for random number generation }