from aeon.classification.interval_based import TimeSeriesForestClassifier
from skopt.space import Categorical
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class TimeSeriesForestClassifier_class:
def __init__(self, ml_grid_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
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self.ml_grid_object = ml_grid_object
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self.method_name = "TimeSeriesForestClassifier"
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 = {
"n_estimators": [10],
"min_interval_length": [3],
"n_jobs": [1],
"random_state": [random_state_val],
}
elif ml_grid_object.global_params.bayessearch:
self.parameter_space = {
"n_estimators": Categorical([50, 100, 200]),
"min_interval_length": Categorical([3, 5]),
"n_jobs": [n_jobs_model_val],
"random_state": [random_state_val],
}
else:
self.parameter_space = {
"n_estimators": [50, 100, 200],
"min_interval_length": [3, 5],
"n_jobs": [n_jobs_model_val],
"random_state": [random_state_val],
}
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self.algorithm_implementation = TimeSeriesForestClassifier()