from typing import Any, Dict, List
from aeon.classification.feature_based import Catch22Classifier
from sklearn.ensemble import RandomForestClassifier
from skopt.space import Categorical
from sklearn.tree import DecisionTreeClassifier
from ml_grid.pipeline.data import pipe
[docs]
class Catch22Classifier_class:
"""A wrapper for the aeon Catch22Classifier time-series classifier.
This class provides a consistent interface for the Catch22Classifier,
including defining a hyperparameter search space.
Attributes:
algorithm_implementation: An instance of the aeon Catch22Classifier.
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: Catch22Classifier
[docs]
parameter_space: Dict[str, List[Any]]
def __init__(self, ml_grid_object: pipe):
"""Initializes the Catch22Classifier_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: Catch22Classifier = Catch22Classifier()
self.method_name: str = "Catch22Classifier"
self.parameter_space: Dict[str, List[Any]]
if getattr(ml_grid_object.global_params, "test_mode", False):
self.parameter_space = {
"features": ["all"],
"estimator": [DecisionTreeClassifier(max_depth=2)],
"n_jobs": [1],
}
return
if ml_grid_object.global_params.bayessearch:
self.parameter_space = {
"features": Categorical(
["all", ("DN_HistogramMode_5", "DN_HistogramMode_10")]
),
"catch24": Categorical([True, False]),
"outlier_norm": Categorical([True, False]),
"replace_nans": Categorical([True, False]),
"use_pycatch22": Categorical([True, False]),
"estimator": Categorical(
[
RandomForestClassifier(n_estimators=200),
DecisionTreeClassifier(),
]
),
"random_state": [random_state_val],
"n_jobs": [n_jobs_model_val],
}
else:
self.parameter_space = {
"features": ["all", ("DN_HistogramMode_5", "DN_HistogramMode_10")],
"catch24": [True, False],
"outlier_norm": [True, False],
"replace_nans": [True, False],
"use_pycatch22": [True, False],
"estimator": [
RandomForestClassifier(n_estimators=200),
DecisionTreeClassifier(),
],
"random_state": [random_state_val],
"n_jobs": [n_jobs_model_val],
}