models.py

from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC
from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor
from sklearn.kernel_ridge import KernelRidge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import KFold, cross_val_score, train_test_split
from sklearn.metrics import mean_squared_error
import xgboost as xgb
import lightgbm as lgb
import numpy as np


#Validation function
n_folds = 5

def getLasso():
    lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))
    return lasso


def getENet():
    ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))
    return ENet

def getKRR():
    KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
    return KRR

def getGBoost():
    GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,
                                   max_depth=4, max_features='sqrt',
                                   min_samples_leaf=15, min_samples_split=10,
                                   loss='huber', random_state =5)
    return GBoost


def getXGB():
    model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468,
                                 learning_rate=0.05, max_depth=3,
                                 min_child_weight=1.7817, n_estimators=2200,
                                 reg_alpha=0.4640, reg_lambda=0.8571,
                                 subsample=0.5213, silent=1,
                                 random_state =7, nthread = -1)
    return model_xgb


def getLGB():
    model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,
                                  learning_rate=0.05, n_estimators=720,
                                  max_bin = 55, bagging_fraction = 0.8,
                                  bagging_freq = 5, feature_fraction = 0.2319,
                                  feature_fraction_seed=9, bagging_seed=9,
                                  min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)
    return model_lgb