from ...performance.metrics import RegressionMetrics
from ..IAlgorithm import IAlgorithm
from sklearn.base import clone
from sklearn.cross_decomposition import PLSRegression as PLSR
[docs]class PLSRegressor(IAlgorithm):
"""Implementation of the PartialLeastSquaresRegressor from the scikitlearn library
"""
def __init__(self,args={},**kwargs):
model = PLSR
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 PLSRegressor(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()
n_components = trial.suggest_int('n_components',1,x_train.shape[1])
max_iter = 1000 #trial.suggest_int('max_iter',200,1000,step=50)
regr = PLSRegressor({'n_components':n_components,'max_iter':max_iter})
regr.fit(x_train, y_train)
y_pred = regr.predict(x_test)
return metric.cmd_rmse(y_test, y_pred)