# Isaac Li
# 1.23.2018
import time
import numpy as np
import xgboost as xgb
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
import function
train, test = function.read_file()
train = train.loc[train['血糖'] > 5]
train["血糖"] = np.log1p(train["血糖"])
train, test = function.transform(train, test)
pred = test.copy()
pred.drop('血糖', axis=1, inplace=True)
model_xgb = xgb.XGBRegressor(colsample_bytree=0.7, gamma=0.15,
learning_rate=0.03, max_depth=5,
min_child_weight=80, n_estimators=1100,
reg_alpha=0.5, reg_lambda=0.7,
subsample=0.9, silent=True,
nthread=8, early_stopping_rounds=100)
'''
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=True,
nthread = 8)'''
print('\n\nStart...')
# Isaac Li
# 1.23.2018
import time
import numpy as np
from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
import function
train, test = function.read_file()
train["血糖"] = np.log1p(train["血糖"])
train, test = function.transform(train, test, fill_na=True)
pred = test.copy()
pred.drop('血糖', axis=1, inplace=True)
KRR = KernelRidge(kernel='polynomial', coef0=0, alpha=0.1, degree=2.1)
print('\n\nStart...')
t0 = time.time()
train_preds, test_preds, mses = np.zeros(train.shape[0]), np.zeros(
(test.shape[0], 5)), []
predictors = [f for f in test.columns if f not in ['血糖']]
kf = KFold(n_splits=5, shuffle=True, random_state=520)
for i, (train_index, test_index) in enumerate(kf.split(train)):
print(' .{}/5.'.format(i + 1))
train_feat1, train_feat2 = train.iloc[train_index], train.iloc[test_index]
gbm = KRR.fit(train_feat1[predictors], train_feat1['血糖'])
predict = gbm.predict(train_feat2[predictors])
train_preds[test_index] += predict