import sys
import os
sys.path.insert(0, os.path.abspath('..'))
from utility.sklearnbasemodel import BaseModel
import numpy as np
from sklearn.ensemble import BaggingRegressor
class BaggingModel(BaseModel):
def __init__(self):
BaseModel.__init__(self)
return
def setClf(self):
# min_samples_split = 3
self.clf = BaggingRegressor(n_estimators = 100, max_samples =0.5, max_features =0.5, verbose = 100)
return
def getTunedParamterOptions(self):
tuned_parameters = [{'min_samples_split': np.arange(2, 1000, 1)}]
# tuned_parameters = [{'min_samples_split': [5, 8,10,12]}]
# tuned_parameters = [{'min_samples_split': [5, 10]}]
return tuned_parameters
if __name__ == "__main__":
obj= BaggingModel()
obj.run()
