from ..IAlgorithm import IAlgorithm
from sklearn.base import clone
from sklearn.ensemble import RandomForestRegressor as RFR
from ...performance.metrics import RegressionMetrics
[docs]class RandomForestRegressor(IAlgorithm):
"""Implementation of the RandomForestRegressor from the scikitlearn library
"""
def __init__(self,args={},**kwargs):
model = RFR
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 RandomForestRegressor(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()
criterion = trial.suggest_categorical('criterion', ['squared_error', 'absolute_error'])
# bootstrap = trial.suggest_categorical('bootstrap',['True','False'])
# max_depth = trial.suggest_int('max_depth', 1, 200)
max_features = trial.suggest_categorical('max_features', ['sqrt','log2'])
# max_leaf_nodes = trial.suggest_int('max_leaf_nodes', 1, 2000)
n_estimators = trial.suggest_int('n_estimators', 30, 300)
regr = RandomForestRegressor({'bootstrap': True, 'criterion': criterion,
'max_features': max_features,
'n_estimators': n_estimators,'n_jobs':2})
regr.fit(x_train, y_train)
y_pred = regr.predict(x_test)
return metric.cmd_rmse(y_test, y_pred)