Python数据分析之财政收入影响因素分析及预测模型

标签：数据分析 1.00 Python NaN 0.99 财政收入 new data reg

# -*- coding: utf-8 -*-

# 代码6-1

import numpy as np
import pandas as pd

inputfile = '../data/data.csv'  # 输入的数据文件
data = pd.read_csv(inputfile)  # 读取数据

# 描述性统计分析
description = [data.min(), data.max(), data.mean(),
               data.std()]  # 依次计算最小值、最大值、均值、标准差
description = pd.DataFrame(
    description, index=['Min', 'Max', 'Mean', 'STD']).T  # 将结果存入数据框
print('描述性统计结果：\n', np.round(description, 2))  # 保留两位小数

描述性统计结果：
             Min         Max        Mean         STD
x1   3831732.00  7599295.00  5579519.95  1262194.72
x2       181.54     2110.78      765.04      595.70
x3       448.19     6882.85     2370.83     1919.17
x4      7571.00    42049.14    19644.69    10203.02
x5      6212.70    33156.83    15870.95     8199.77
x6   6370241.00  8323096.00  7350513.60   621341.85
x7       525.71     4454.55     1712.24     1184.71
x8       985.31    15420.14     5705.80     4478.40
x9        60.62      228.46      129.49       50.51
x10       65.66      852.56      340.22      251.58
x11       97.50      120.00      103.31        5.51
x12        1.03        1.91        1.42        0.25
x13     5321.00    41972.00    17273.80    11109.19
y         64.87     2088.14      618.08      609.25

# 代码6-2

# 相关性分析
corr = data.corr(method='pearson')  # 计算相关系数矩阵
print('相关系数矩阵为：\n', np.round(corr, 2))  # 保留两位小数

相关系数矩阵为：
        x1    x2    x3    x4    x5    x6    x7    x8    x9   x10   x11   x12  \
x1   1.00  0.95  0.95  0.97  0.97  0.99  0.95  0.97  0.98  0.98 -0.29  0.94   
x2   0.95  1.00  1.00  0.99  0.99  0.92  0.99  0.99  0.98  0.98 -0.13  0.89   
x3   0.95  1.00  1.00  0.99  0.99  0.92  1.00  0.99  0.98  0.99 -0.15  0.89   
x4   0.97  0.99  0.99  1.00  1.00  0.95  0.99  1.00  0.99  1.00 -0.19  0.91   
x5   0.97  0.99  0.99  1.00  1.00  0.95  0.99  1.00  0.99  1.00 -0.18  0.90   
x6   0.99  0.92  0.92  0.95  0.95  1.00  0.93  0.95  0.97  0.96 -0.34  0.95   
x7   0.95  0.99  1.00  0.99  0.99  0.93  1.00  0.99  0.98  0.99 -0.15  0.89   
x8   0.97  0.99  0.99  1.00  1.00  0.95  0.99  1.00  0.99  1.00 -0.15  0.90   
x9   0.98  0.98  0.98  0.99  0.99  0.97  0.98  0.99  1.00  0.99 -0.23  0.91   
x10  0.98  0.98  0.99  1.00  1.00  0.96  0.99  1.00  0.99  1.00 -0.17  0.90   
x11 -0.29 -0.13 -0.15 -0.19 -0.18 -0.34 -0.15 -0.15 -0.23 -0.17  1.00 -0.43   
x12  0.94  0.89  0.89  0.91  0.90  0.95  0.89  0.90  0.91  0.90 -0.43  1.00   
x13  0.96  1.00  1.00  1.00  0.99  0.94  1.00  1.00  0.99  0.99 -0.16  0.90   
y    0.94  0.98  0.99  0.99  0.99  0.91  0.99  0.99  0.98  0.99 -0.12  0.87   

      x13     y  
x1   0.96  0.94  
x2   1.00  0.98  
x3   1.00  0.99  
x4   1.00  0.99  
x5   0.99  0.99  
x6   0.94  0.91  
x7   1.00  0.99  
x8   1.00  0.99  
x9   0.99  0.98  
x10  0.99  0.99  
x11 -0.16 -0.12  
x12  0.90  0.87  
x13  1.00  0.99  
y    0.99  1.00  

import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['font.sans-serif'] = ['SimHei']  # 中文标签
plt.rcParams['axes.unicode_minus'] = False  # 正常显示负号

plt.subplots(figsize=(12, 12))
sns.heatmap(corr, annot=True, vmax=1, square=True, cmap="Reds")
plt.title('相关性热力图(20信计1班李之琛3322)')
plt.savefig('../imag/hot.jpg', dpi=600)
plt.show()

# 代码6-3

# 绘制热力图
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['font.sans-serif'] = ['SimHei']  # 中文标签
plt.rcParams['axes.unicode_minus'] = False  # 正常显示负号
plt.subplots(figsize=(10, 10))  # 设置画面大小
sns.heatmap(corr, annot=True, vmax=1, square=True, cmap="Blues")
plt.title('相关性热力图')
plt.show()
# plt.close

import numpy as np
import pandas as pd
from sklearn.linear_model import Lasso

inputfile = '../data/data.csv'  # 输入的数据文件
data = pd.read_csv(inputfile)  # 读取数据
lasso = Lasso(1000)  # 调用Lasso()函数，设置λ的值为1000
lasso.fit(data.iloc[:,0:13],data['y'])
print('相关系数为：',np.round(lasso.coef_,5))  # 输出结果，保留五位小数

相关系数为： [-1.8000e-04 -0.0000e+00  1.2414e-01 -1.0310e-02  6.5400e-02  1.2000e-04
  3.1741e-01  3.4900e-02 -0.0000e+00  0.0000e+00  0.0000e+00  0.0000e+00
 -4.0300e-02]


F:\Anaconda\envs\my\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.417e+04, tolerance: 7.053e+02
  model = cd_fast.enet_coordinate_descent(

lasso.coef_

array([-1.76147790e-04, -0.00000000e+00,  1.24143041e-01, -1.03120575e-02,
        6.53999569e-02,  1.15234764e-04,  3.17411469e-01,  3.49002210e-02,
       -0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
       -4.02991430e-02])

print('相关系数非零个数为：',np.sum(lasso.coef_ != 0))  # 计算相关系数非零的个数

相关系数非零个数为： 8

mask = lasso.coef_ != 0  # 返回一个相关系数是否为零的布尔数组
print('相关系数是否为零：',mask)

相关系数是否为零： [ True False  True  True  True  True  True  True False False False False
  True]

data.head(5)

	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10	x11	x12	x13	y
0	3831732	181.54	448.19	7571.00	6212.70	6370241	525.71	985.31	60.62	65.66	120.0	1.029	5321	64.87
1	3913824	214.63	549.97	9038.16	7601.73	6467115	618.25	1259.20	73.46	95.46	113.5	1.051	6529	99.75
2	3928907	239.56	686.44	9905.31	8092.82	6560508	638.94	1468.06	81.16	81.16	108.2	1.064	7008	88.11
3	4282130	261.58	802.59	10444.60	8767.98	6664862	656.58	1678.12	85.72	91.70	102.2	1.092	7694	106.07
4	4453911	283.14	904.57	11255.70	9422.33	6741400	758.83	1893.52	88.88	114.61	97.7	1.200	8027	137.32

mask = np.append(mask,True)
mask

array([ True, False,  True,  True,  True,  True,  True,  True, False,
       False, False, False,  True,  True])

outputfile ='../tmp/new_reg_data_2.csv'  # 输出的数据文件
new_reg_data = data.iloc[:, mask]  # 返回相关系数非零的数据
new_reg_data

	x1	x3	x4	x5	x6	x7	x8	x13	y
0	3831732	448.19	7571.00	6212.70	6370241	525.71	985.31	5321	64.87
1	3913824	549.97	9038.16	7601.73	6467115	618.25	1259.20	6529	99.75
2	3928907	686.44	9905.31	8092.82	6560508	638.94	1468.06	7008	88.11
3	4282130	802.59	10444.60	8767.98	6664862	656.58	1678.12	7694	106.07
4	4453911	904.57	11255.70	9422.33	6741400	758.83	1893.52	8027	137.32
5	4548852	1000.69	12018.52	9751.44	6850024	878.26	2139.18	8549	188.14
6	4962579	1121.13	13966.53	11349.47	7006896	923.67	2492.74	9566	219.91
7	5029338	1248.29	14694.00	11467.35	7125979	978.21	2841.65	10473	271.91
8	5070216	1370.68	13380.47	10671.78	7206229	1009.24	3203.96	11469	269.10
9	5210706	1494.27	15002.59	11570.58	7251888	1175.17	3758.62	12360	300.55
10	5407087	1677.77	16884.16	13120.83	7376720	1348.93	4450.55	14174	338.45
11	5744550	1905.84	18287.24	14468.24	7505322	1519.16	5154.23	16394	408.86
12	5994973	2199.14	19850.66	15444.93	7607220	1696.38	6081.86	17881	476.72
13	6236312	2624.24	22469.22	18951.32	7734787	1863.34	7140.32	20058	838.99
14	6529045	3187.39	25316.72	20835.95	7841695	2105.54	8287.38	22114	843.14
15	6791495	3615.77	27609.59	22820.89	7946154	2659.85	9138.21	24190	1107.67
16	7110695	4476.38	30658.49	25011.61	8061370	3263.57	10748.28	29549	1399.16
17	7431755	5243.03	34438.08	28209.74	8145797	3412.21	12423.44	34214	1535.14
18	7512997	5977.27	38053.52	30490.44	8222969	3758.39	13551.21	37934	1579.68
19	7599295	6882.85	42049.14	33156.83	8323096	4454.55	15420.14	41972	2088.14

new_reg_data = new_reg_data.iloc[:,:-1]
new_reg_data

	x1	x3	x4	x5	x6	x7	x8	x13
0	3831732	448.19	7571.00	6212.70	6370241	525.71	985.31	5321
1	3913824	549.97	9038.16	7601.73	6467115	618.25	1259.20	6529
2	3928907	686.44	9905.31	8092.82	6560508	638.94	1468.06	7008
3	4282130	802.59	10444.60	8767.98	6664862	656.58	1678.12	7694
4	4453911	904.57	11255.70	9422.33	6741400	758.83	1893.52	8027
5	4548852	1000.69	12018.52	9751.44	6850024	878.26	2139.18	8549
6	4962579	1121.13	13966.53	11349.47	7006896	923.67	2492.74	9566
7	5029338	1248.29	14694.00	11467.35	7125979	978.21	2841.65	10473
8	5070216	1370.68	13380.47	10671.78	7206229	1009.24	3203.96	11469
9	5210706	1494.27	15002.59	11570.58	7251888	1175.17	3758.62	12360
10	5407087	1677.77	16884.16	13120.83	7376720	1348.93	4450.55	14174
11	5744550	1905.84	18287.24	14468.24	7505322	1519.16	5154.23	16394
12	5994973	2199.14	19850.66	15444.93	7607220	1696.38	6081.86	17881
13	6236312	2624.24	22469.22	18951.32	7734787	1863.34	7140.32	20058
14	6529045	3187.39	25316.72	20835.95	7841695	2105.54	8287.38	22114
15	6791495	3615.77	27609.59	22820.89	7946154	2659.85	9138.21	24190
16	7110695	4476.38	30658.49	25011.61	8061370	3263.57	10748.28	29549
17	7431755	5243.03	34438.08	28209.74	8145797	3412.21	12423.44	34214
18	7512997	5977.27	38053.52	30490.44	8222969	3758.39	13551.21	37934
19	7599295	6882.85	42049.14	33156.83	8323096	4454.55	15420.14	41972

new_reg_data.to_csv(outputfile)  # 存储数据
print('输出数据的维度为：',new_reg_data.shape)  # 查看输出数据的维度

输出数据的维度为： (20, 8)

#-*- coding: utf-8 -*-

def GM11(x0): #自定义灰色预测函数
  import numpy as np
  x1 = x0.cumsum() #1-AGO序列
  z1 = (x1[:len(x1)-1] + x1[1:])/2.0 #紧邻均值（MEAN）生成序列
  z1 = z1.reshape((len(z1),1))
  B = np.append(-z1, np.ones_like(z1), axis = 1)
  Yn = x0[1:].reshape((len(x0)-1, 1))
  [[a],[b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn) #计算参数
  f = lambda k: (x0[0]-b/a)*np.exp(-a*(k-1))-(x0[0]-b/a)*np.exp(-a*(k-2)) #还原值
  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)
  return f, a, b, x0[0], C, P #返回灰色预测函数、a、b、首项、方差比、小残差概率

# 代码6-5

# import sys
# sys.path.append('../code')  # 设置路径
import numpy as np
import pandas as pd
# from GM11 import GM11  # 引入自编的灰色预测函数

inputfile1 = '../tmp/new_reg_data_2.csv'  # 输入的数据文件
inputfile2 = '../data/data.csv'  # 输入的数据文件
new_reg_data = pd.read_csv(inputfile1)  # 读取经过特征选择后的数据
data = pd.read_csv(inputfile2)  # 读取总的数据
new_reg_data.index = range(1994, 2014)
new_reg_data.loc[2014] = None
new_reg_data.loc[2015] = None
l = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']

C:\Windows\Temp\ipykernel_63584\756966604.py:6: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  new_reg_data.loc[2014] = None
C:\Windows\Temp\ipykernel_63584\756966604.py:7: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  new_reg_data.loc[2015] = None

new_reg_data

	Unnamed: 0	x1	x3	x4	x5	x6	x7	x8	x13
1994	0.0	3831732.0	448.19	7571.00	6212.70	6370241.0	525.71	985.31	5321.0
1995	1.0	3913824.0	549.97	9038.16	7601.73	6467115.0	618.25	1259.20	6529.0
1996	2.0	3928907.0	686.44	9905.31	8092.82	6560508.0	638.94	1468.06	7008.0
1997	3.0	4282130.0	802.59	10444.60	8767.98	6664862.0	656.58	1678.12	7694.0
1998	4.0	4453911.0	904.57	11255.70	9422.33	6741400.0	758.83	1893.52	8027.0
1999	5.0	4548852.0	1000.69	12018.52	9751.44	6850024.0	878.26	2139.18	8549.0
2000	6.0	4962579.0	1121.13	13966.53	11349.47	7006896.0	923.67	2492.74	9566.0
2001	7.0	5029338.0	1248.29	14694.00	11467.35	7125979.0	978.21	2841.65	10473.0
2002	8.0	5070216.0	1370.68	13380.47	10671.78	7206229.0	1009.24	3203.96	11469.0
2003	9.0	5210706.0	1494.27	15002.59	11570.58	7251888.0	1175.17	3758.62	12360.0
2004	10.0	5407087.0	1677.77	16884.16	13120.83	7376720.0	1348.93	4450.55	14174.0
2005	11.0	5744550.0	1905.84	18287.24	14468.24	7505322.0	1519.16	5154.23	16394.0
2006	12.0	5994973.0	2199.14	19850.66	15444.93	7607220.0	1696.38	6081.86	17881.0
2007	13.0	6236312.0	2624.24	22469.22	18951.32	7734787.0	1863.34	7140.32	20058.0
2008	14.0	6529045.0	3187.39	25316.72	20835.95	7841695.0	2105.54	8287.38	22114.0
2009	15.0	6791495.0	3615.77	27609.59	22820.89	7946154.0	2659.85	9138.21	24190.0
2010	16.0	7110695.0	4476.38	30658.49	25011.61	8061370.0	3263.57	10748.28	29549.0
2011	17.0	7431755.0	5243.03	34438.08	28209.74	8145797.0	3412.21	12423.44	34214.0
2012	18.0	7512997.0	5977.27	38053.52	30490.44	8222969.0	3758.39	13551.21	37934.0
2013	19.0	7599295.0	6882.85	42049.14	33156.83	8323096.0	4454.55	15420.14	41972.0
2014	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2015	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

import xlwt

for i in l:
  f = GM11(new_reg_data.loc[range(1994, 2014),i].values)[0]
  new_reg_data.loc[2014,i] = f(len(new_reg_data)-1)  # 2014年预测结果
  new_reg_data.loc[2015,i] = f(len(new_reg_data))  # 2015年预测结果
  new_reg_data[i] = new_reg_data[i].round(2)  # 保留两位小数
outputfile = '../tmp/new_reg_data_GM11_2.xls'  # 灰色预测后保存的路径
y = list(data['y'].values)  # 提取财政收入列，合并至新数据框中
y.extend([np.nan,np.nan])
new_reg_data['y'] = y
new_reg_data.to_excel(outputfile)  # 结果输出
print('预测结果为：\n',new_reg_data.loc[2014:2015,:])  # 预测结果展示

预测结果为：
       Unnamed: 0          x1       x3        x4        x5          x6  \
2014         NaN  8142148.24  7042.31  43611.84  35046.63  8505522.58   
2015         NaN  8460489.28  8166.92  47792.22  38384.22  8627139.31   

           x7        x8       x13   y  
2014  4600.40  18686.28  44506.47 NaN  
2015  5214.78  21474.47  49945.88 NaN  


C:\Windows\Temp\ipykernel_63584\1948140384.py:10: FutureWarning: As the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.
  new_reg_data.to_excel(outputfile)  # 结果输出

# 代码6-6

import matplotlib.pyplot as plt
from sklearn.svm import LinearSVR

inputfile = '../tmp/new_reg_data_GM11_2.xls'  # 灰色预测后保存的路径
data = pd.read_excel(inputfile)  # 读取数据
feature = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']  # 属性所在列

data

	Unnamed: 0.1	Unnamed: 0	x1	x3	x4	x5	x6	x7	x8	x13	y
0	1994	0.0	3831732.00	448.19	7571.00	6212.70	6370241.00	525.71	985.31	5321.00	64.87
1	1995	1.0	3913824.00	549.97	9038.16	7601.73	6467115.00	618.25	1259.20	6529.00	99.75
2	1996	2.0	3928907.00	686.44	9905.31	8092.82	6560508.00	638.94	1468.06	7008.00	88.11
3	1997	3.0	4282130.00	802.59	10444.60	8767.98	6664862.00	656.58	1678.12	7694.00	106.07
4	1998	4.0	4453911.00	904.57	11255.70	9422.33	6741400.00	758.83	1893.52	8027.00	137.32
5	1999	5.0	4548852.00	1000.69	12018.52	9751.44	6850024.00	878.26	2139.18	8549.00	188.14
6	2000	6.0	4962579.00	1121.13	13966.53	11349.47	7006896.00	923.67	2492.74	9566.00	219.91
7	2001	7.0	5029338.00	1248.29	14694.00	11467.35	7125979.00	978.21	2841.65	10473.00	271.91
8	2002	8.0	5070216.00	1370.68	13380.47	10671.78	7206229.00	1009.24	3203.96	11469.00	269.10
9	2003	9.0	5210706.00	1494.27	15002.59	11570.58	7251888.00	1175.17	3758.62	12360.00	300.55
10	2004	10.0	5407087.00	1677.77	16884.16	13120.83	7376720.00	1348.93	4450.55	14174.00	338.45
11	2005	11.0	5744550.00	1905.84	18287.24	14468.24	7505322.00	1519.16	5154.23	16394.00	408.86
12	2006	12.0	5994973.00	2199.14	19850.66	15444.93	7607220.00	1696.38	6081.86	17881.00	476.72
13	2007	13.0	6236312.00	2624.24	22469.22	18951.32	7734787.00	1863.34	7140.32	20058.00	838.99
14	2008	14.0	6529045.00	3187.39	25316.72	20835.95	7841695.00	2105.54	8287.38	22114.00	843.14
15	2009	15.0	6791495.00	3615.77	27609.59	22820.89	7946154.00	2659.85	9138.21	24190.00	1107.67
16	2010	16.0	7110695.00	4476.38	30658.49	25011.61	8061370.00	3263.57	10748.28	29549.00	1399.16
17	2011	17.0	7431755.00	5243.03	34438.08	28209.74	8145797.00	3412.21	12423.44	34214.00	1535.14
18	2012	18.0	7512997.00	5977.27	38053.52	30490.44	8222969.00	3758.39	13551.21	37934.00	1579.68
19	2013	19.0	7599295.00	6882.85	42049.14	33156.83	8323096.00	4454.55	15420.14	41972.00	2088.14
20	2014	NaN	8142148.24	7042.31	43611.84	35046.63	8505522.58	4600.40	18686.28	44506.47	NaN
21	2015	NaN	8460489.28	8166.92	47792.22	38384.22	8627139.31	5214.78	21474.47	49945.88	NaN

data_train=data.loc[0:19,:]
data_train.head()

	Unnamed: 0.1	Unnamed: 0	x1	x3	x4	x5	x6	x7	x8	x13	y
0	1994	0.0	3831732.0	448.19	7571.00	6212.70	6370241.0	525.71	985.31	5321.0	64.87
1	1995	1.0	3913824.0	549.97	9038.16	7601.73	6467115.0	618.25	1259.20	6529.0	99.75
2	1996	2.0	3928907.0	686.44	9905.31	8092.82	6560508.0	638.94	1468.06	7008.0	88.11
3	1997	3.0	4282130.0	802.59	10444.60	8767.98	6664862.0	656.58	1678.12	7694.0	106.07
4	1998	4.0	4453911.0	904.57	11255.70	9422.33	6741400.0	758.83	1893.52	8027.0	137.32

data_mean = data_train.mean()
data_std = data_train.std()

data_train = (data_train - data_mean)/data_std  # 数据标准化
x_train = data_train[feature].values  # 属性数据
y_train = data_train['y'].values  # 标签数据

data_train[feature]

	x1	x3	x4	x5	x6	x7	x8	x13
0	-1.384721	-1.001807	-1.183344	-1.177868	-1.577670	-1.001532	-1.054057	-1.075938
1	-1.319682	-0.948774	-1.039547	-1.008469	-1.421759	-0.923420	-0.992899	-0.967199
2	-1.307732	-0.877665	-0.954558	-0.948579	-1.271451	-0.905956	-0.946262	-0.924082
3	-1.027884	-0.817144	-0.901702	-0.866240	-1.103501	-0.891067	-0.899357	-0.862331
4	-0.891787	-0.764006	-0.822206	-0.786439	-0.980320	-0.804759	-0.851259	-0.832356
5	-0.816568	-0.713922	-0.747442	-0.746302	-0.805498	-0.703950	-0.796405	-0.785368
6	-0.488784	-0.651165	-0.556517	-0.551415	-0.553025	-0.665620	-0.717457	-0.693822
7	-0.435893	-0.584907	-0.485218	-0.537039	-0.361370	-0.619583	-0.639547	-0.612178
8	-0.403507	-0.521135	-0.613957	-0.634063	-0.232215	-0.593391	-0.558646	-0.522522
9	-0.292201	-0.456737	-0.454973	-0.524450	-0.158730	-0.453332	-0.434793	-0.442319
10	-0.136614	-0.361123	-0.270560	-0.335390	0.042177	-0.306664	-0.280290	-0.279030
11	0.130748	-0.242285	-0.133043	-0.171067	0.249152	-0.162975	-0.123162	-0.079196
12	0.329151	-0.089458	0.020188	-0.051955	0.413148	-0.013386	0.083972	0.054657
13	0.520357	0.132044	0.276833	0.375666	0.618457	0.127542	0.320320	0.250621
14	0.752281	0.425479	0.555917	0.605505	0.790517	0.331980	0.576452	0.435693
15	0.960212	0.648690	0.780642	0.847578	0.958636	0.799865	0.766437	0.622566
16	1.213105	1.097119	1.079465	1.114746	1.144067	1.309456	1.125956	1.104959
17	1.467472	1.496590	1.449903	1.504773	1.279945	1.434921	1.500009	1.524882
18	1.531837	1.879172	1.804253	1.782915	1.404147	1.727127	1.751833	1.859740
19	1.600209	2.351033	2.195865	2.108093	1.565294	2.314745	2.169154	2.223223

linearsvr = LinearSVR()  # 调用LinearSVR()函数
linearsvr.fit(x_train,y_train)
x = ((data[feature] - data_mean[feature])/data_std[feature]).values  # 预测，并还原结果。
data['y_pred'] = linearsvr.predict(x) * data_std['y'] + data_mean['y']
outputfile = '../tmp/new_reg_data_GM11_revenue_2.xls'  # SVR预测后保存的结果
data.to_excel(outputfile)

print('真实值与预测值分别为：\n',data[['y','y_pred']])

fig = data[['y','y_pred']].plot(subplots = True, style=['b-o','r-*'])  # 画出预测结果图
plt.show()

F:\Anaconda\envs\my\lib\site-packages\sklearn\svm\_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
C:\Windows\Temp\ipykernel_63584\2363168573.py:6: FutureWarning: As the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.
  data.to_excel(outputfile)


真实值与预测值分别为：
           y       y_pred
0     64.87    37.302597
1     99.75    83.928503
2     88.11    94.774134
3    106.07   106.434331
4    137.32   151.059897
5    188.14   188.205356
6    219.91   219.566685
7    271.91   230.340346
8    269.10   219.659959
9    300.55   300.550000
10   338.45   383.515443
11   408.86   463.222420
12   476.72   554.905404
13   838.99   691.434167
14   843.14   843.095183
15  1107.67  1088.163763
16  1399.16  1379.593271
17  1535.14  1536.823098
18  1579.68  1739.224541
19  2088.14  2086.022177
20      NaN  2188.120305
21      NaN  2539.228245

标签：数据分析,1.00,Python,NaN,0.99,财政收入,new,data,reg
From： https://www.cnblogs.com/Reion/p/17181371.html