from typing import Any, Dict, List
from aeon.classification.deep_learning import EncoderClassifier
from ml_grid.pipeline.data import pipe
[docs]
class EncoderClassifier_class:
"""A wrapper for the aeon EncoderClassifier time-series classifier.
This class provides a consistent interface for the EncoderClassifier,
including defining a hyperparameter search space.
Attributes:
algorithm_implementation: An instance of the aeon EncoderClassifier.
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: EncoderClassifier
[docs]
parameter_space: Dict[str, List[Any]]
def __init__(self, ml_grid_object: pipe):
"""Initializes the EncoderClassifier_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
self.algorithm_implementation = EncoderClassifier()
self.method_name = "EncoderClassifier"
self.parameter_space = {
"kernel_size": [
[5],
[11],
[21],
[5, 11],
[5, 21],
[11, 21],
[5, 11, 21],
], # Specifying the length of the 1D convolution windows
"n_filters": [
[128],
[256],
[512],
[128, 256],
[128, 512],
[256, 512],
[128, 256, 512],
], # Specifying the number of 1D convolution filters used for each layer
"max_pool_size": [2, 3], # Size of the max pooling windows
"activation": ["sigmoid", "relu", "tanh"], # Keras activation function
"dropout_proba": [0.0, 0.2, 0.5], # Dropout layer probability
"padding": ["same", "valid"], # Type of padding used for 1D convolution
"strides": [1, 2], # Sliding rate of the 1D convolution filter
"fc_units": [
128,
256,
512,
], # Number of units in the hidden fully connected layer
#'file_path': ['./', './models/'], # File path when saving the model_Checkpoint callback
"save_best_model": [False], # Whether or not to save the best model
"save_last_model": [False], # Whether or not to save the last model
#'best_file_name': ['best_model', 'best_classifier'], # Name of the file of the best model
#'last_file_name': ['last_model', 'last_classifier'], # Name of the file of the last model
"random_state": [random_state_val], # Seed for any needed random actions
}