/
base.py
136 lines (111 loc) · 4.34 KB
/
base.py
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#!/usr/bin/env python
"""
Created by Dai at 18-9-27.
"""
import pandas as pd
from statsmodels.sandbox.stats.diagnostic import acorr_ljungbox
from statsmodels.tsa.stattools import adfuller
import numpy as np
class BaseModel():
def __init__(self, dataset: dict):
"""
针对单元 分析
:param dataset:
"""
self.dataset = pd.DataFrame(data=dataset, dtype=np.float)
self.key = self.dataset.keys()[0]
self.data = self.dataset[self.key]
def stationarity(self, data=None):
"""
平稳性检测 ADF检验的p<0.01
:return: (Ture,p)
"""
if data is None:
data = self.data
pval = adfuller(data.dropna(), autolag='AIC')
return (True, pval[1]) if pval[1] < 0.01 else (False, pval[1])
def randomness(self, data=None):
"""
随机性检测
默认情况下, acorr_ljungbox只计算LB统计量, 只有当参数boxpierce=True时, 才会输出Q统计量.
一般如果统计量的P值小于0.05时，则可以拒绝原假设，认为该序列为非白噪声序列，跟Q统计量差不多。
:return:
"""
if data is None:
data = self.data
lbvalue, pval = acorr_ljungbox(data.dropna(), lags=True)
return (True, pval[0]) if pval[0] > 0.05 else (False, pval[0])
def diff_data(self, O_data=None, interval=1):
"""
对数据进行差分
:param dataset: 原始数据
:param interval: 差分间隔
:return: 差分数据
"""
if O_data is None:
O_data = self.data
diff = list()
start, stop = self.get_start_stop(O_data.keys())
# 获差分对应索引
###差分后的前interval个数应为空
for i in range(start + interval):
diff.append(None)
for i in range(start + interval, stop):
value = O_data[i] - O_data[i - interval]
diff.append(value)
return pd.Series(diff).dropna()
def restore_data(self, D_data, O_data, interval):
"""
根据d阶差分与记录下的值 对差分进行还原
:param D_data: 差分数据
:param O_Data: 原始数据
:param interval: 差分间隔
:return:
"""
start, stop = self.get_start_stop(D_data.keys())
# 获取差分对应索引
_, stop_index = self.get_start_stop(O_data.keys())
### 还原差分
for i in range(start, stop):
if i - interval < stop_index:
D_data[i] = D_data[i] + O_data[i - interval]
else:
D_data[i] = D_data[i] + D_data[i - interval]
return D_data
def get_start_stop(self, index):
try:
start = index._start
stop = index._stop
except:
start = index[0]
stop = index[-1] + 1
return start, stop
if __name__ == "__main__":
data = {
"黑子": [601, 604, 620, 626, 641, 642, 645, 655, 682, 678, 692, 707,
736, 753, 763, 775, 775, 783, 794, 813, 823, 826, 829, 831,
830, 838, 854, 872, 882, 903, 919, 937, 927, 962, 975, 995,
1001, 1013, 1021, 1028, 1027, 1048, 1070, 1095, 1113, 1143, 1154, 1173,
1178, 1183, 1205, 1208, 1209, 1223, 1238, 1245, 1258, 1278, 1294, 1314,
1323, 1336, 1355, 1377, 1416, 1430, 1455, 1480, 1514, 1545, 1589, 1634,
1669, 1715, 1760, 1812, 1809, 1828, 1871, 1892, 1946, 1983, 2013, 2045,
2048, 2097, 2140, 2171, 2208, 2272, 2311, 2349, 2362, 2442, 2479, 2528,
2571, 2634, 2684, 2790, 2890, 2964, 3085, 3159, 3237, 3358, 3489, 3588,
3624, 3719, 3821, 3934, 4028, 4129, 4205, 4349, 4463, 4598, 4725, 4827,
4939, 5067, 5231, 5408, 5492, 5653, 5828, 5965,
]
}
model = BaseModel(dataset=data)
import matplotlib.pyplot as plt
model.data.plot()
diff1 = model.diff_data(interval=4)
diff1.plot()
diff2 = model.diff_data(O_data=diff1, interval=1)
diff2.plot()
restore = model.restore_data(D_data=diff2, O_data=diff1, interval=1)
restore = model.restore_data(D_data=restore, O_data=model.data, interval=4)
print(restore)
restore.plot()
plt.show()
print(model.stationarity())
print(model.randomness())