from sklearn.svm import SVR
from ..IAlgorithm import IAlgorithm
from sklearn.base import clone as skclone
from ...performance.metrics import RegressionMetrics
[docs]class SupportVectorRegression(IAlgorithm):
"""Implementation of the SupportVectorRegression from the scikitlearn library
"""
def __init__(self,args={},**kwargs):
model = SVR
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):
if(self.model.fit_status_ != 0):
raise TypeError("The model is not fitted yet")
return self.model.predict(x_values)
[docs] def get_model(self):
return self.model
[docs] def clone(self):
return SupportVectorRegression(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()
kernel=trial.suggest_categorical('kernel',['rbf','sigmoid'])
c=trial.suggest_float("C",0.1,3.0,step=0.5)
gamma=trial.suggest_categorical('gamma',['auto','scale'])
#degree=trial.suggest_int("degree",1,3)
regr = SupportVectorRegression({'kernel': kernel, 'C': c, 'gamma': gamma})
regr.fit(x_train, y_train)
y_pred = regr.predict(x_test)
return metric.cmd_rmse(y_test, y_pred)