from typing import Any, Dict, List
from aeon.classification.feature_based._fresh_prince import FreshPRINCEClassifier
from skopt.space import Categorical
from ml_grid.pipeline.data import pipe
[docs]
class FreshPRINCEClassifier_class:
"""A wrapper for the aeon FreshPRINCEClassifier time-series classifier.
This class provides a consistent interface for the FreshPRINCEClassifier,
including defining a hyperparameter search space.
Attributes:
algorithm_implementation: An instance of the aeon FreshPRINCEClassifier.
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: FreshPRINCEClassifier
[docs]
parameter_space: Dict[str, List[Any]]
def __init__(self, ml_grid_object: pipe):
"""Initializes the FreshPRINCEClassifier_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
verbose_param = ml_grid_object.verbose
n_jobs_model_val = ml_grid_object.global_params.n_jobs_model_val
self.algorithm_implementation = FreshPRINCEClassifier()
self.method_name = "FreshPRINCEClassifier"
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 = {
"default_fc_parameters": ["minimal"],
"n_estimators": [10],
"verbose": [0],
"n_jobs": [1],
"chunksize": [None],
"random_state": [random_state_val],
}
elif ml_grid_object.global_params.bayessearch:
self.parameter_space = {
"default_fc_parameters": Categorical(
["minimal", "efficient", "comprehensive"]
), # Set of TSFresh features to be extracted
"n_estimators": Categorical(
[100, 200, 300]
), # Number of estimators for the RotationForestClassifier ensemble
"verbose": [
verbose_param
], # Level of output printed to the console (for information only)
"n_jobs": [n_jobs_model_val], # Number of jobs for parallel processing
"chunksize": Categorical(
[None, 100, 200]
), # Number of series processed in each parallel TSFresh job
"random_state": [random_state_val], # Seed for random, integer
}
else:
self.parameter_space = {
"default_fc_parameters": [
"minimal",
"efficient",
"comprehensive",
], # Set of TSFresh features to be extracted
"n_estimators": [
100,
200,
300,
], # Number of estimators for the RotationForestClassifier ensemble
"verbose": [verbose_param],
"n_jobs": [n_jobs_model_val],
"chunksize": [
None,
100,
200,
], # Number of series processed in each parallel TSFresh job
"random_state": [random_state_val],
}