import sys
import os
sys.path.insert(0, os.path.abspath('..'))
# from pprint import pprint as p
# p(sys.path)
# print os.environ['PYTHONPATH'].split(os.pathsep)
from utility.sklearnbasemodel import BaseModel
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing
from sklearn.pipeline import Pipeline
from preprocess.preparedata import HoldoutSplitMethod
class LinearRegressionModel(BaseModel):
def __init__(self):
BaseModel.__init__(self)
# self.usedFeatures = [101,102,103,4,5,6, 701,702,703,801,802,901,902]
self.holdout_split = HoldoutSplitMethod.IMITTATE_TEST2_PLUS1
self.save_final_model = False
self.do_cross_val = False
return
def setClf(self):
# self.clf = Ridge(alpha=0.0000001, tol=0.0000001)
clf = LinearRegression()
min_max_scaler = preprocessing.MinMaxScaler()
self.clf = Pipeline([('scaler', min_max_scaler), ('estimator', clf)])
return
def after_train(self):
print "self.clf.named_steps['estimator'].coef_:\n{}".format(self.clf.named_steps['estimator'].coef_)
print "self.clf.named_steps['estimator'].intercept_:\n{}".format(self.clf.named_steps['estimator'].intercept_)
return
if __name__ == "__main__":
obj= LinearRegressionModel()
obj.run()
