commit chapter11

chapter11/code.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Nov  4 11:04:32 2017
+
+@author: lu
+"""
+
+import pandas as pd
+from statsmodels.tsa.stattools import adfuller as ADF
+from statsmodels.stats.diagnostic import acorr_ljungbox
+from statsmodels.tsa.arima_model import ARIMA
+
+"""
+FutureWarning警告：原因未知，在spyder3上运行第二次就消失了，猜测是使用了缓存的原因
+attr_trans-->属性变换
+programmer_1-->数据筛选
+programmer_2-->平稳性检测
+programmer_3-->白噪声检测
+programmer_4-->确定最佳p、d、q值，有问题！！！
+programmer_5-->模型检验
+programmer_6-->计算预测误差
+"""
+
+
+# 属性变换,改变列名
+def attr_trans(x):
+    result = pd.Series(
+        index=["SYS_NAME", "CWXT_DB:184:C:\\", "CWXT_DB:184:D:\\", "COLLECTTIME"])
+    result["SYS_NAME"] = x["SYS_NAME"].iloc[0]
+    result["COLLECTTIME"] = x["COLLECTTIME"].iloc[0]
+    result["CWXT_DB:184:C:\\"] = x["VALUE"].iloc[0]
+    result["CWXT_DB:184:D:\\"] = x["VALUE"].iloc[1]
+
+    return result
+
+
+def programmer_1():
+
+    discfile = "data/discdata.xls"
+    transformeddata = "tmp/discdata_processed.xls"
+
+    data = pd.read_excel(discfile)
+    # 提取某部分数据
+    data = data[data["TARGET_ID"] == 184].copy()
+    # 以某字段进行分组
+    data_group = data.groupby("COLLECTTIME")
+
+    # 逐组处理
+    data_processed = data_group.apply(attr_trans)
+    data_processed.to_excel(transformeddata, index=False)
+
+
+def programmer_2():
+    discfile = "data/discdata_processed.xls"
+    data = pd.read_excel(discfile)
+    # 去除最后5个数据
+    predictnum = 5
+    data = data.iloc[:len(data) - predictnum]
+
+    # 平稳性检测
+    diff = 0
+    adf = ADF(data["CWXT_DB:184:D:\\"])
+    while adf[1] > 0.05:
+        diff = diff + 1
+        adf = ADF(data["CWXT_DB:184:D:\\"].diff(diff).dropna())
+
+    print(u"原始序列经过%s阶差分后归于平稳，p值为%s" % (diff, adf[1]))
+
+
+def programmer_3():
+
+    discfile = "data/discdata_processed.xls"
+
+    data = pd.read_excel(discfile)
+    data = data.iloc[:len(data) - 5]
+
+    [[lb], [p]] = acorr_ljungbox(data["CWXT_DB:184:D:\\"], lags=1)
+    if p < 0.05:
+        print(u"原始序列为非白噪声序列，对应的p值为：%s" % p)
+    else:
+        print(u"原始序列为白噪声序列，对应的p值为：%s" % p)
+
+    [[lb], [p]] = acorr_ljungbox(
+        data["CWXT_DB:184:D:\\"].diff().dropna(), lags=1)
+
+    if p < 0.05:
+        print(u"一阶差分序列为非白噪声序列，对应的p值为：%s" % p)
+    else:
+        print(u"一阶差分序列为白噪声序列，对应的p值为：%s" % p)
+
+
+def programmer_4():
+    discfile = "data/discdata_processed.xls"
+
+    data = pd.read_excel(discfile, index_col="COLLECTTIME")
+    # 不使用最后五个数据
+    data = data.iloc[:len(data) - 5]
+    xdata = data["CWXT_DB:184:D:\\"]
+
+    # 定阶
+    pmax = int(len(xdata) / 10)
+    qmax = int(len(xdata) / 10)
+    # 定义bic矩阵
+    bic_matrix = []
+    for p in range(pmax + 1):
+        tmp = []
+        for q in range(qmax + 1):
+            try:
+                tmp.append(ARIMA(xdata, (p, 1, q)).fit().bic)
+            except:
+                tmp.append(None)
+        bic_matrix.append(tmp)
+
+    bic_matrix = pd.DataFrame(bic_matrix)
+    # 找出最小值
+    p, q = bic_matrix.stack().idxmin()
+    print(u"BIC最小的p值和q值为：%s、%s" % (p, q))
+
+
+def programmer_5():
+    discfile = "data/discdata_processed.xls"
+    # 残差延迟个数
+    lagnum = 12
+
+    data = pd.read_excel(discfile, index_col="COLLECTTIME")
+    data = data.iloc[:len(data) - 5]
+    xdata = data["CWXT_DB:184:D:\\"]
+
+    # 训练模型并预测，计算残差
+    arima = ARIMA(xdata, (0, 1, 1)).fit()
+    xdata_pred = arima.predict(typ="levels")
+    pred_error = (xdata_pred - xdata).dropna()
+
+    lb, p = acorr_ljungbox(pred_error, lags=lagnum)
+    h = (p < 0.05).sum()
+    if h > 0:
+        print(u"模型ARIMA（0,1,1)不符合白噪声检验")
+    else:
+        print(u"模型ARIMA（0,1,1)符合白噪声检验")
+
+
+def programmer_6():
+
+    file = "data/predictdata.xls"
+    data = pd.read_excel(file)
+
+    # 计算误差
+    abs_ = (data[u"预测值"] - data[u"实际值"]).abs()
+    mae_ = abs_.mean()
+    rmse_ = ((abs_ ** 2).mean()) ** 0.5
+    mape_ = (abs_/data[u"实际值"]).mean()
+
+    print(u"平均绝对误差为：%0.4f, \n 均方根误差为%0.4f, \n平均绝对百分误差为：%0.6f。" % (mae_, rmse_, mape_))
+
+
+
+if __name__ == "__main__":
+    # programmer_1()
+    # programmer_2()
+    # programmer_3()
+    # programmer_4()
+    # programmer_5()
+    # programmer_6()