def main():
url = "http://dcfm.eastmoney.com/em_mutisvcexpandinterface/api/js/get?type=KZZ_LB2.0&token=70f12f2f4f091e459a279469fe49eca5" # 东方财富
print(url)
json_data = get_data(url)
subscribe_reminder(json_data)
winning_reminder(json_data)
print("*" * 40 + "\n\n\n")
def main():
# url = "http://dcfm.eastmoney.com/em_mutisvcexpandinterface/api/js/get?type=KZZ_LB2.0&token=70f12f2f4f091e459a279469fe49eca5" # 东方财富
url = "http://dcfm.eastmoney.com/em_mutisvcexpandinterface/api/js/get?type=KZZ_LB2.0&token=70f12f2f4f091e459a279469fe49eca5&p=1&st=STARTDATE&sr=-1&ps=50"
print(url)
json_data = get_data(url)
icals = valid_bonds(json_data)
# subscribe_reminder(json_data)
# winning_reminder(json_data)
print("*" * 40 + "\n\n\n")
'''
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
import xgboost as xgb
from sklearn.model_selection import StratifiedKFold
from sklearn import metrics
from get_all_data import get_data
from xgboost import plot_importance
from matplotlib import pyplot as plt
bst2 = xgb.Booster(model_file='./model/xgb.model')
train_X, test_X, train_y, test_y = get_data(ues_smote=True)
# print(test_X.shape)
# random = np.random.random((1,135))
# print(random)
dtest = xgb.DMatrix(test_X) #np.array([[0]*135])
y_pred = bst2.predict(dtest)
# print('test_y', test_y,len(test_y))
# print('dtest', dtest.num_col())
# print('y_pred',y_pred)
pred_list = []
for pred in y_pred:
pred_list.append(pred[1])
from xgboost import XGBClassifier
import xgboost as xgb
import pandas as pd
import numpy as np
from pylab import mpl
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedKFold
from sklearn import metrics
from xgboost import plot_importance
import matplotlib.pyplot as plt
import uuid
from get_all_data import get_data
X_train, X_test, y_train, y_test = get_data(ues_smote=True)
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)
useTrainCV = True
cv_folds = None
early_stopping_rounds = 50
xgb1 = XGBClassifier(
alpha=1, # L1正则化系数，默认为1
seed=4, # 随机种子 复现
scale_pos_weight=1, # 正样本的权重，在二分类任务中，当正负样本比例失衡时，设置正样本的权重，模型效果更好。例如，当正负样本比例为1:10时scale_pos_weight=10。
num_class=2,
nthread=-1, # nthread=-1时，使用全部CPU进行并行运算（默认）nthread=1时，使用1个CPU进行运算。
silent=1, # silent=0时，不输出中间过程（默认）silent=1时，输出中间过程
subsample=0.8, # 使用的数据占全部训练集的比例。防止overfitting。默认值为1，典型值为0.5-1。