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import numpy as np
from numpy import ndarray
class GradientBoostingRegressor():
'''
Class Description:
GBDT class, which stores the trained GBDT.
'''
def __init__(self):
'''
Function Description:
Initialize the GBDT.
'''
raise NotImplementedError('GradientBoostingRegressor __init__ method should be implemented')
def fit(self, data: ndarray,
label: ndarray,
n_estimators: int,
learning_rate: float,
max_depth: int,
min_samples_split: int,
subsample=None):
'''
Function Description:
Train the GBDT based on the given decision variable neural encoding and optimal solution values.
Parameters:
- data: Neural encoding results of the decision variables.
- label: Values of the decision variables in the optimal solution.
- n_estimators: Number of decision trees.
- learning_rate: Learning rate.
- max_depth: Maximum depth of the decision trees.
- min_samples_split: Minimum number of samples required to split a leaf node.
- subsample: Subsample rate without replacement.
Return:
The training results are stored in the class. There is no return value.
'''
raise NotImplementedError('GradientBoostingRegressor fit method should be implemented')
def predict(self, data: ndarray) -> ndarray:
'''
Function Description:
Use the trained GBDT to predict the initial solution based on the given decision variable neural encoding, and return the predicted initial solution.
Parameters:
- data: Neural encoding results of the decision variables.
Return:
The predicted initial solution.
'''
raise NotImplementedError('GradientBoostingRegressor predict method should be implemented')
def calc(self, data: ndarray) -> ndarray:
'''
Function Description:
Use the trained GBDT to predict the initial solution based on the given decision variable neural encoding, and return the prediction loss.
Parameters:
- data: Neural encoding results of the decision variables.
Return:
The prediction loss generated when predicting the initial solution for each decision variable.
'''
raise NotImplementedError('GradientBoostingRegressor calc method should be implemented')
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