from ..IAlgorithm import IAlgorithm
from sklearn.base import clone
from sklearn.ensemble import GradientBoostingRegressor as GBR
from ...performance.metrics import RegressionMetrics
[docs]class GradientBoostingRegressor(IAlgorithm):
"""Implementation of the GradientBoostingRegressor from the scikitlearn library
"""
def __init__(self,args={},**kwargs):
model = GBR
if args:
self.model = model(**args)
elif kwargs:
self.model = model(**kwargs)
else:
self.model = model()
[docs] def fit(self,x_values,y_values,*args):
return self.model.fit(x_values,y_values,*args)
[docs] def predict(self,x_values):
return self.model.predict(x_values)
[docs] def get_model(self):
return self.model
[docs] def clone(self):
return GradientBoostingRegressor(self.get_params())
[docs] def get_params(self):
return self.model.get_params()
[docs] def set_params(self,params):
return self.model.set_params(**params)
[docs] def objective_function(self,trial,x_train,y_train,x_test,y_test):
metric = RegressionMetrics()
loss = trial.suggest_categorical('loss',['squared_error', 'absolute_error', 'huber', 'quantile'])
learning_rate = trial.suggest_float('learning_rate',0.05,0.5,step=0.05)
n_estimators = trial.suggest_int('n_estimators', 200, 1000,step=100)
regr= GradientBoostingRegressor({'loss' : loss, 'learning_rate' : learning_rate,'n_estimators' : n_estimators})
regr.fit(x_train, y_train)
y_pred = regr.predict(x_test)
return metric.cmd_rmse(y_test, y_pred)