python_data_analysis13
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 8 23:05:54 2017
@author: lu
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from keras.layers.core import Activation, Dense
from keras.models import Sequential
from sklearn.linear_model import Lasso
"""
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
programmer_4-->使用自定义的灰度预测模型进行预测一组数据，并且画图
"""
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
data[["y", "y_pred"]].plot(subplots=True, style=["b-o", "r-*"])
plt.show()
def programmer_4():
x0 = np.array([3152063, 2213050, 4050122,
5265142, 5556619, 4772843, 9463330])
f, a, b, x00, C, P = GM11(x0)
print(a, b, x00, C, P)
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()
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# )
# programmer_4()
pass
#携程暑期实习#
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Python由荷兰数学和计算机科学研究学会的吉多·范罗苏姆于1990年代初设计，作为一门叫做ABC语言的替代品。Python提供了高效的高级数据结构，还能简单有效地面向对象编程。Python语法和动态类型，以及解释型语言的本质，使它成为多数平台上写脚本和快速开发应用的编程语言，随着版本的不断更新和语言新功能的添加，逐渐被用于独立的、大型项目的开发
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