From a2ac723361fe33ef6c5af65d376b424d2d0efbb4 Mon Sep 17 00:00:00 2001 From: wnma3mz Date: Thu, 9 Nov 2017 14:25:09 +0800 Subject: [PATCH] commit chapter13 --- chapter13/code.py | 236 +++++++++++++++++++++++++++ chapter13/data/VAT.xls | Bin 0 -> 5632 bytes chapter13/data/data1.csv | 21 +++ chapter13/data/data2.csv | 16 ++ chapter13/data/data3.csv | 16 ++ chapter13/data/data4.csv | 13 ++ chapter13/data/data5.csv | 15 ++ chapter13/data/enterprise_income.xls | Bin 0 -> 5632 bytes chapter13/data/enterprise_incomt.xls | Bin 0 -> 5632 bytes chapter13/data/personal_Income.xls | Bin 0 -> 5632 bytes chapter13/data/revenue.xls | Bin 0 -> 5632 bytes chapter13/data/sales_tax.xls | Bin 0 -> 5632 bytes chapter13/tmp/1-net.model | Bin 0 -> 13944 bytes chapter13/tmp/2-net.model | Bin 0 -> 13944 bytes chapter13/tmp/3-net.model | Bin 0 -> 13936 bytes chapter13/tmp/4-net.model | Bin 0 -> 13936 bytes chapter13/tmp/5-net.model | Bin 0 -> 13936 bytes chapter13/tmp/data1_GM11.xls | Bin 0 -> 5632 bytes chapter13/tmp/data2_GM11.xls | Bin 0 -> 5632 bytes chapter13/tmp/data3_GM11.xls | Bin 0 -> 5632 bytes chapter13/tmp/data4_GM11.xls | Bin 0 -> 5632 bytes chapter13/tmp/data5_GM11.xls | Bin 0 -> 5632 bytes 22 files changed, 317 insertions(+) create mode 100755 chapter13/code.py create mode 100755 chapter13/data/VAT.xls create mode 100755 chapter13/data/data1.csv create mode 100755 chapter13/data/data2.csv create mode 100755 chapter13/data/data3.csv create mode 100755 chapter13/data/data4.csv create mode 100755 chapter13/data/data5.csv create mode 100755 chapter13/data/enterprise_income.xls create mode 100755 chapter13/data/enterprise_incomt.xls create mode 100755 chapter13/data/personal_Income.xls create mode 100755 chapter13/data/revenue.xls create mode 100755 chapter13/data/sales_tax.xls create mode 100755 chapter13/tmp/1-net.model create mode 100755 chapter13/tmp/2-net.model create mode 100755 chapter13/tmp/3-net.model create mode 100755 chapter13/tmp/4-net.model create mode 100755 chapter13/tmp/5-net.model create mode 100755 chapter13/tmp/data1_GM11.xls create mode 100755 chapter13/tmp/data2_GM11.xls create mode 100755 chapter13/tmp/data3_GM11.xls create mode 100755 chapter13/tmp/data4_GM11.xls create mode 100755 chapter13/tmp/data5_GM11.xls diff --git a/chapter13/code.py b/chapter13/code.py new file mode 100755 index 0000000..c513c20 --- /dev/null +++ b/chapter13/code.py @@ -0,0 +1,236 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Nov 8 23:05:54 2017 + +@author: lu +""" + +import pandas as pd +import numpy as np +from sklearn.linear_model import Lasso +from keras.models import Sequential +from keras.layers.core import Dense, Activation +import matplotlib.pyplot as plt + +""" +GM11-->自定义的灰度预测函 +programmer_1-->读取文件提取基本信息 +programmer_2-->用自定义的灰度预测函数，进行预测 +programmer_3-->建立神经网络模型，进行预测并画图预测图 +""" + + +def GM11(x0): + # 1-AGO序列, 累计求和 + x1 = np.cumsum(x0) + # 紧邻均值（ＭＥＡＮ）生成序列 + z1 = (x1[:-1] + x1[1:]) / 2.0 + z1 = z1.reshape(len(z1), 1) + B = np.append(-z1, np.ones_like(z1), axis=1) + Yn = x0[1:].reshape((len(x0) - 1, 1)) + # 矩阵计算，计算参数 + [[a], [b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn) + # 还原值 + f = lambda k: (x0[0] - b / a) * np.exp(-a * (k - 1)) -( x0[0] - b / a) * np.exp(-a * (k - 2)) + + delta=np.abs(x0 - np.array([f(i) for i in range(1, len(x0) + 1)])) + C=delta.std() / x0.std() + P=1.0 * (np.abs(delta - delta.mean()) < + 0.6745 * x0.std()).sum() / len(x0) + # 灰度预测函数、a、b、首项、方差比、小残差概率 + + return f, a, b, x0[0], C, P + + +def programmer_1(inputfile, data_range): + # inputfile = "data/data1.csv" + data=pd.read_csv(inputfile) + """ + 原始方法，替代方法可以使用describe()方法，然后进行筛选 + r = [data.min(), data.max(), data.mean(), data.std()] + r = pd.DataFrame(r, index = ["Min", "Max", "Mean", "STD"]).T + """ + r=pd.DataFrame(data.describe()).T + np.round(r, 2) + + # 计算相关系数矩阵 + np.round(data.corr(method="pearson"), 2) + + """ + 原代码使用的是AdaptiveLasso，现更新为Lasso + 参数也由gamma变为tol（有待验证） + """ + model =Lasso(tol = 1) + model.fit(data.iloc[:, 0:data_range], data["y"]) + # 各个特征的系数 + model.coef_ + print(model.coef_) + + +def programmer_2(inputfile, outputfile, startyear, feature_lst, roundnum = 0): + """ + year： 开始年份 + feature_lst: 特征列 + roundnum： 四舍五入保留的位数 + """ + data=pd.read_csv(inputfile) + data.index=range(startyear, 2014) + + data.loc[2014]=None + data.loc[2015]=None + for i in feature_lst: + f=GM11(data[i][list(range(startyear, 2014))].as_matrix())[0] + # 2014年预测结果 + data[i][2014]=f(len(data) - 1) + # 2015年预测结果 + data[i][2015]=f(len(data)) + data[i]=data[i].round(roundnum) + + print(data[feature_lst + ["y"]]) + data[feature_lst + ["y"]].to_excel(outputfile) + + +def programmer_3(inputfile, outputfile, modelfile, feature_lst, startyear, input_dim_1, units1, input_dim_2, units2, epochs_num = 10000, roundnum = 0): + """ + feature_lst: 特征列 + input_dim、units: 表示训练模型层数和神经元个数 + roundnum: 四舍五入 + """ + + data=pd.read_excel(inputfile) + # 特征列 + # 取startyear年以前的数据 + data_train=data.loc[range(startyear, 2014)].copy() + data_mean=data_train.mean() + data_std=data.std() + # 数据标准化 + data_train=(data_train - data_mean) / data_std + # 特征数据 + x_train=data_train[feature_lst].as_matrix() + # 标签数据 + y_train=data_train["y"].as_matrix() + + model=Sequential() + model.add(Dense(input_dim=input_dim_1, units=units1)) + model.add(Activation("relu")) + model.add(Dense(input_dim=input_dim_2, units=units2)) + model.compile(loss = "mean_squared_error", optimizer = "adam") + model.fit(x_train, y_train, epochs = epochs_num, batch_size = 16) + model.save_weights(modelfile) + + # 预测，并且还原结果 + x=((data[feature_lst] - data_mean[feature_lst]) / + data_std[feature_lst]).as_matrix() + data["y_pred"]=model.predict(x) * data_std["y"] + data_mean["y"] + data["y_pred"]=data["y_pred"].round(roundnum) + + data.to_excel(outputfile) + # 画出预测结果图 + p=data[["y", "y_pred"]].plot(subplots = True, style = ["b-o", "r-*"]) + plt.show() + + +if __name__ == "__main__": + # programmer_1(inputfile="data/data1.csv", + # data_range=13) + # programmer_2(inputfile="data/data1.csv", + # outputfile="tmp/data1_GM11.xls", + # startyear=1994, + # feature_lst=["x1", "x2", "x3", "x4", "x5", "x7"], + # roundnum=2) + # programmer_3(inputfile="tmp/data1_GM11.xls", + # outputfile="data/revenue.xls", + # modelfile="tmp/1-net.model", + # feature_lst=["x1", "x2", "x3", "x4", "x5", "x7"], + # startyear=1994, + # input_dim_1=6, + # units1=12, + # input_dim_2=12, + # units2=1) + + # programmer_1(inputfile="data/data2.csv", + # data_range=6) + # programmer_2(inputfile="data/data2.csv", + # outputfile="tmp/data2_GM11.xls", + # startyear=1999, + # feature_lst=["x1", "x3", "x5"], + # roundnum=6) + # programmer_3(inputfile="tmp/data2_GM11.xls", + # outputfile="data/VAT.xls", + # modelfile="tmp/2-net.model", + # feature_lst=["x1", "x3", "x5"], + # startyear=1999, + # input_dim_1=3, + # units1=6, + # input_dim_2=6, + # units2=1, + # roundnum=2) + + # programmer_1(inputfile="data/data3.csv", + # data_range=10) + # programmer_2(inputfile="data/data3.csv", + # outputfile="tmp/data3_GM11.xls", + # startyear=1999, + # feature_lst=["x3", "x4", "x6", "x8"]) + # programmer_3(inputfile="tmp/data3_GM11.xls", + # outputfile="data/sales_tax.xls", + # modelfile="tmp/3-net.model", + # feature_lst=["x3", "x4", "x6", "x8"], + # startyear=1999, + # input_dim_1=4, + # units1=8, + # input_dim_2=8, + # units2=1, + # roundnum=2) + + # programmer_1(inputfile="data/data4.csv", + # data_range=10) + # programmer_2(inputfile="data/data4.csv", + # outputfile="tmp/data4_GM11.xls", + # startyear=2002, + # feature_lst=["x1", "x2", "x3", "x4", "x6", "x7", "x9", "x10"], + # roundnum=2) + # programmer_3(inputfile="tmp/data4_GM11.xls", + # outputfile="data/enterprise_incomt.xls", + # modelfile="tmp/4-net.model", + # feature_lst=["x1", "x2", "x3", "x4", "x6", "x7", "x9", "x10"], + # startyear=2002, + # input_dim_1=8, + # units1=6, + # input_dim_2=6, + # units2=1, + # roundnum=2) + + # programmer_1(inputfile="data/data5.csv", + # data_range=7) + # programmer_2(inputfile="data/data5.csv", + # outputfile="tmp/data5_GM11.xls", + # startyear=2000, + # feature_lst=["x1", "x4", "x5", "x7"]) + # programmer_3(inputfile="tmp/data5_GM11.xls", + # outputfile="data/personal_Income.xls", + # modelfile="tmp/5-net.model", + # feature_lst=["x1", "x4", "x5", "x7"], + # startyear=2000, + # input_dim_1=4, + # units1=8, + # input_dim_2=8, + # units2=1, + # epochs_num=15000# ) + + x0=np.array([3152063, 2213050, 4050122, + 5265142, 5556619, 4772843, 9463330]) + f, a, b, x00, C, P=GM11(x0) + + print(u'2014年、2015年的预测结果分别为：\n%0.2f万元和%0.2f万元' % (f(8), f(9))) + print(u'后验差比值为：%0.4f' % C) + p=pd.DataFrame(x0, columns = ["y"], index = range(2007, 2014)) + p.loc[2014]=None + p.loc[2015]=None + p["y_pred"]=[f(i) for i in range(1, 10)] + p["y_pred"]=p["y_pred"].round(2) + p.index =pd.to_datetime(p.index, format = "%Y") + + p.plot(style = ["b-o", "r-*"], xticks = p.index) + plt.show() diff --git a/chapter13/data/VAT.xls 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