XGBRegressor

from sklearn.preprocessing import LabelEncoder
from collections import defaultdict
from sklearn.utils import shuffle
import pandas as pd
import numpy as np
import xgboost as xgb
from xgboost.sklearn import XGBClassifier,XGBRegressor
import matplotlib.pylab as plt
train = pd.read_csv('train.csv')

train=train.drop(train.columns[0],axis=1)
train = shuffle(train )

target = ['change','code','date']

test=pd.read_csv('test.csv')

test=test.drop(test.columns[0],axis=1)

x_train=train.drop(target,axis=1).values

y_train=train['change'].values
x_test=test.drop(target,axis=1).values



xgre=XGBRegressor(max_depth=30, learning_rate=0.01, n_estimators=5, 
             silent=True, objective='reg:linear', booster='gblinear', n_jobs=50, 
             nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, 
             colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
             scale_pos_weight=1, base_score=0.5, random_state=0, seed=None,
             missing=None, importance_type='gain')
print ("training...")
xgre.fit(x_train, y_train, verbose=True)

xgre.save_model('tree100.model')

print('training is ok')
fit_pred = xgre.predict(x_test)

#tar = xgb.Booster(model_file='tree100.model')
#x_test1 = xgb.DMatrix(x_test)
#fit_pred1 = tar.predict(x_test1)

inde=list(train.drop(target,axis=1).columns)


ww=(xgre.feature_importances_)
print(ww)            
feat_imp = pd.Series(ww,index=inde).sort_values(ascending=False)

feat_imp.to_excel('feature_importance.xlsx')

#print(feat_imp)
#plt.set_size_inches(20,10) 
feat_imp.plot(kind='bar', title='Feature Importances')