diff --git a/水稻叶片病害分类/平时作业/病马数据集/data_horse.csv b/水稻叶片病害分类/平时作业/病马数据集/data_horse.csv new file mode 100644 index 0000000..673bca8 --- /dev/null +++ b/水稻叶片病害分类/平时作业/病马数据集/data_horse.csv @@ -0,0 +1,367 @@ +ֱ¶,,Ƶ,ϸ,ֵܵ,y +38.5,66,28,45,8.4,0 +39.2,88,20,50,85,0 +38.3,40,24,33,6.7,1 +39.1,164,84,48,7.2,0 +37.3,104,35,74,7.4,0 +0,0,0,0,0,1 +37.9,48,16,37,7,1 +0,60,0,44,8.3,0 +0,80,36,38,6.2,0 +38.3,90,0,40,6.2,1 +38.1,66,12,44,6,1 +39.1,72,52,50,7.8,1 +37.2,42,12,0,7,1 +38,92,28,37,6.1,0 +38.2,76,28,46,81,1 +37.6,96,48,45,6.8,0 +0,128,36,53,7.8,0 +37.5,48,24,0,0,1 +37.6,64,21,40,7,1 +39.4,110,35,55,8.7,1 +39.9,72,60,46,6.1,1 +38.4,48,16,49,6.8,1 +38.6,42,34,48,7.2,1 +38.3,130,60,50,70,1 +38.1,60,12,51,65,1 +37.8,60,42,0,0,1 +38.3,72,30,43,7,1 +37.8,48,12,37,5.5,1 +0,0,0,0,0,0 +37.7,48,0,45,76,1 +37.7,96,30,66,7.5,0 +37.2,108,12,52,8.2,0 +37.2,60,0,43,6.6,1 +38.2,64,28,49,8.6,1 +0,100,30,52,6.6,1 +0,104,24,73,8.4,0 +38.3,112,16,51,6,0 +37.8,72,0,56,80,1 +38.6,52,0,32,6.6,1 +39.2,146,96,0,0,0 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+38.8,50,20,42,6.2,1 +0,0,0,38,6.5,0 +37.5,48,30,48,8.6,1 +37.3,48,20,41,69,1 +0,84,36,44,8.5,1 +38.1,88,32,55,60,0 +37.7,44,40,41,60,1 +39.6,108,51,59,8,1 +38.2,40,16,34,66,1 +0,60,20,0,0,0 +38.3,40,16,37,57,1 +38,140,68,39,5.3,1 +37.8,52,24,48,6.6,0 +0,70,36,36,7.3,1 +38.3,52,96,43,6.1,1 +37.3,50,32,44,7,1 +38.7,60,32,53,64,0 +38.4,84,40,36,6.6,0 +0,70,16,60,7.5,0 +38.3,40,16,38,58,1 +0,40,0,39,56,1 +36.8,60,28,0,0,0 +38.4,44,24,50,77,1 +0,0,40,45,70,1 +38,44,12,42,65,1 +39.5,0,0,0,6.7,0 +36.5,78,30,34,75,1 +38.1,56,20,46,70,1 +39.4,54,66,39,6,1 +38.3,80,40,67,10.2,0 +38.7,40,28,39,62,1 +38.2,64,24,45,7.5,0 +37.6,48,20,37,5.5,0 +38,42,68,41,7.6,1 +38.7,0,0,33,6.5,1 +37.4,50,32,45,7.9,1 +37.4,84,20,31,61,0 +38.4,49,0,44,7.6,1 +37.8,30,12,0,0,0 +37.6,88,36,44,6,0 +37.9,40,24,40,5.7,1 +0,100,0,59,6.3,0 +38.1,136,48,33,4.9,0 +0,0,0,46,5.9,0 +38,48,0,0,0,1 +38,56,0,42,71,1 +38,60,32,50,7,1 +38.1,44,9,31,7.3,1 +36,42,30,64,6.8,0 +0,120,0,57,4.5,0 +37.8,48,28,46,5.9,1 +37.1,84,40,75,81,0 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+38.5,96,36,70,8.5,0 +38.3,60,20,34,66,1 +38.5,60,40,49,59,1 +37.3,48,12,40,6.6,1 +38.5,86,0,45,7.4,0 +37.5,48,40,41,55,0 +37.2,36,9,35,5.7,1 +39.2,0,23,36,6.6,1 +38.5,100,0,0,0,0 +38.5,96,30,50,65,1 +0,0,0,45,8.7,0 +37.8,88,80,64,89,0 +37.5,44,10,43,51,1 +37.9,68,20,45,4,0 +38,86,24,45,5.5,0 +38.9,120,30,47,6.3,1 +37.6,45,12,39,7,1 +38.6,56,32,40,7,1 +37.8,40,12,38,7,1 +0,0,0,0,0,1 +38,76,18,71,11,1 +38.1,40,36,0,0,0 +0,52,28,37,8.1,1 +39.2,88,58,0,0,0 +38.5,92,40,46,67,1 +0,112,13,60,6.3,1 +37.7,66,12,31.5,6.2,1 +38.8,50,14,38,58,1 +38.4,54,24,49,7.2,1 +39.2,120,20,60,8.8,0 +0,0,0,45,6.5,1 +37.3,90,40,65,50,0 +38.5,120,70,35,54,1 +38.5,104,40,0,0,1 +39.5,92,28,72,6.4,0 +38.5,30,18,40,7.7,1 +38.3,72,30,43,7,1 +37.5,48,30,48,8.6,1 +38.1,52,24,54,7.5,0 +38.2,42,26,36,6.9,1 +37.9,54,42,47,54,1 +36.1,88,0,45,7,0 +38.1,70,22,36,65,0 +38,90,30,55,6.1,0 +38.2,52,16,43,8.1,1 +0,36,32,41,5.9,0 +38.4,92,20,0,0,1 +38.2,124,88,47,8,1 +0,96,0,60,0,0 +37.6,68,32,47,7.2,1 +38.1,88,24,41,4.6,0 +38,108,60,0,0,1 +38.2,48,0,34,6.6,1 +39.3,100,51,66,13,0 +36.6,42,18,52,7.1,0 +38.8,124,36,50,7.6,0 +0,112,24,40,5.3,1 +0,80,0,43,70,1 +38.8,184,84,33,3.3,0 +37.5,72,0,35,65,0 +38.7,96,28,64,9,0 +37.5,52,12,36,61,1 +40.8,72,42,54,7.4,0 +38,40,25,37,69,1 +38.4,48,16,39,6.5,1 +38.6,88,28,35,5.9,1 +37.1,75,36,48,7.4,0 +38.3,44,21,44,6.5,1 +0,56,68,40,6,0 +38.6,68,20,38,6.5,1 +38.3,54,18,44,7.2,1 +38.2,42,20,47,60,1 +39.3,64,90,39,6.7,1 +37.5,60,50,35,6.5,0 +37.7,80,0,50,55,1 +0,100,30,52,6.6,1 +37.7,120,28,65,7,0 +0,76,0,0,0,0 +38.8,150,50,50,6.2,0 +38,36,16,37,75,0 +36.9,50,40,37.5,6.5,1 +37.8,40,16,37,6.8,1 +38.2,56,40,47,7.2,1 +38.6,48,12,36,67,1 +40,78,0,66,6.5,0 +0,70,16,60,7.5,0 +38.2,72,18,35,6.4,1 +38.5,54,0,40,6.8,1 +38.5,66,24,40,6.7,1 +37.8,82,12,50,7,0 +39.5,84,30,28,5,1 +0,0,0,0,0,1 +38,50,36,39,6.6,1 +38.6,45,16,43,58,1 +38.9,80,44,54,6.5,0 +37,66,20,35,6.9,0 +0,78,24,43,62,0 +38.5,40,16,37,67,1 +0,120,70,55,65,0 +37.2,72,24,44,0,0 +37.5,72,30,60,6.8,0 +36.5,100,24,50,6,1 +37.2,40,20,36,62,0 +38.5,54,20,42,6.3,1 +37.6,48,36,44,6.3,1 +37.7,44,28,45,70,1 +37,56,24,35,61,0 +38,42,12,37,5.8,1 +0,60,40,42,72,1 +38.4,80,60,54,6.9,1 +37.8,48,12,48,7.3,1 +37.9,45,36,33,5.7,1 +39,84,12,62,5.9,0 +38.2,60,24,53,7.5,1 +0,140,0,30,69,0 +37.9,120,60,52,6.6,0 +38,72,36,38,6.8,1 +38,92,28,37,6.1,1 +38.3,66,30,37,6,1 +37.5,48,24,43,6,1 +37.5,88,20,35,6.4,0 +0,150,60,0,0,0 +39.7,100,30,65,75,0 +38.3,80,0,45,7.5,1 +37.5,40,32,32,6.4,1 +38.4,84,30,47,7.5,0 +38.1,84,44,60,6.8,0 +38.7,52,0,4,74,1 +38.1,44,40,35,6.8,1 +38.4,52,20,41,63,1 +38.2,60,0,43,6.2,1 +37.7,40,18,36,3.5,1 +39.1,60,10,0,0,1 +37.8,48,16,43,7.5,1 +39,120,0,65,8.2,1 +38.2,76,0,35,6.5,1 +38.3,88,0,0,0,0 +38,80,30,48,8.3,1 +0,0,0,0,0,0 +37.6,40,0,0,0,1 +37.5,44,0,45,5.8,1 +38.2,42,16,35,60,1 +38,56,44,47,70,1 +38.3,45,20,0,0,1 +0,48,96,42,8,1 +37.7,55,28,0,0,1 +36,100,20,74,5.7,0 +37.1,60,20,64,8.5,1 +37.1,114,40,32,0,1 +38.1,72,30,37,56,1 +37,44,12,40,6.7,1 +38.6,48,20,37,75,1 +0,82,72,53,65,0 +38.2,78,60,59,5.8,0 +37.8,60,16,41,73,0 +38.7,34,30,33,69,0 +0,36,12,44,0,1 +38.3,44,60,6.4,36,1 +37.4,54,18,30,7.1,1 +0,0,0,54,76,1 +36.6,48,16,27,56,0 +38.5,90,0,47,79, +0,75,12,58,8.5, +38.2,42,0,35,5.9, +38.2,78,60,59,5.8, +38.6,60,30,40,6, +37.8,42,40,36,6.2, +38,60,12,44,65, +38,42,12,37,5.8, +37.6,88,36,44,6, diff --git a/水稻叶片病害分类/平时作业/病马数据集/your_report.html b/水稻叶片病害分类/平时作业/病马数据集/your_report.html new file mode 100644 index 0000000..5aa35e9 --- /dev/null +++ b/水稻叶片病害分类/平时作业/病马数据集/your_report.html @@ -0,0 +1,14246 @@ +Pandas Profiling Report

Overview

Dataset statistics

Number of variables6
Number of observations366
Missing cells9
Missing cells (%)0.4%
Duplicate rows17
Duplicate rows (%)4.6%
Total size in memory17.3 KiB
Average record size in memory48.3 B

Variable types

Numeric5
Categorical1

Alerts

Dataset has 17 (4.6%) duplicate rowsDuplicates
y has 9 (2.5%) missing valuesMissing
直肠温度 has 69 (18.9%) zerosZeros
脉搏 has 26 (7.1%) zerosZeros
呼吸频率 has 71 (19.4%) zerosZeros
红细胞体积 has 36 (9.8%) zerosZeros
总蛋白值 has 42 (11.5%) zerosZeros

Reproduction

Analysis started2023-07-16 01:23:35.657398
Analysis finished2023-07-16 01:23:38.953735
Duration3.3 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

直肠温度
Real number (ℝ)

Distinct41
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.945902
Minimum0
Maximum40.8
Zeros69
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-07-16T09:23:39.027759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q137.2
median38
Q338.4
95-th percentile39.275
Maximum40.8
Range40.8
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation14.950152
Coefficient of variation (CV)0.48310603
Kurtosis0.5500296
Mean30.945902
Median Absolute Deviation (MAD)0.5
Skewness-1.5921352
Sum11326.2
Variance223.50704
MonotonicityNot monotonic
2023-07-16T09:23:39.126498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 69
18.9%
38 32
 
8.7%
38.3 23
 
6.3%
38.2 23
 
6.3%
37.8 21
 
5.7%
38.5 21
 
5.7%
37.5 16
 
4.4%
38.1 15
 
4.1%
38.4 14
 
3.8%
38.6 14
 
3.8%
Other values (31) 118
32.2%
ValueCountFrequency (%)
0 69
18.9%
35.4 1
 
0.3%
36 2
 
0.5%
36.1 1
 
0.3%
36.4 1
 
0.3%
36.5 2
 
0.5%
36.6 2
 
0.5%
36.8 1
 
0.3%
36.9 1
 
0.3%
37 4
 
1.1%
ValueCountFrequency (%)
40.8 1
 
0.3%
40.3 2
 
0.5%
40 1
 
0.3%
39.9 1
 
0.3%
39.7 2
 
0.5%
39.6 1
 
0.3%
39.5 4
1.1%
39.4 3
0.8%
39.3 4
1.1%
39.2 5
1.4%

脉搏
Real number (ℝ)

Distinct55
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.808743
Minimum0
Maximum184
Zeros26
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-07-16T09:23:39.235038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148
median60
Q386
95-th percentile120
Maximum184
Range184
Interquartile range (IQR)38

Descriptive statistics

Standard deviation32.676103
Coefficient of variation (CV)0.49653133
Kurtosis0.62195244
Mean65.808743
Median Absolute Deviation (MAD)18
Skewness0.36778858
Sum24086
Variance1067.7277
MonotonicityNot monotonic
2023-07-16T09:23:39.336060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 34
 
9.3%
60 33
 
9.0%
0 26
 
7.1%
40 21
 
5.7%
44 16
 
4.4%
42 15
 
4.1%
88 15
 
4.1%
52 14
 
3.8%
72 13
 
3.6%
100 13
 
3.6%
Other values (45) 166
45.4%
ValueCountFrequency (%)
0 26
7.1%
30 2
 
0.5%
34 1
 
0.3%
36 4
 
1.1%
38 1
 
0.3%
40 21
5.7%
42 15
4.1%
44 16
4.4%
45 4
 
1.1%
46 1
 
0.3%
ValueCountFrequency (%)
184 1
 
0.3%
164 1
 
0.3%
160 1
 
0.3%
150 3
0.8%
146 1
 
0.3%
140 3
0.8%
136 1
 
0.3%
132 1
 
0.3%
130 2
0.5%
129 1
 
0.3%

呼吸频率
Real number (ℝ)

Distinct41
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.603825
Minimum0
Maximum96
Zeros71
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-07-16T09:23:39.437199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median21.5
Q335
95-th percentile64.5
Maximum96
Range96
Interquartile range (IQR)23

Descriptive statistics

Standard deviation19.968438
Coefficient of variation (CV)0.81159891
Kurtosis1.6528179
Mean24.603825
Median Absolute Deviation (MAD)10.5
Skewness1.1088325
Sum9005
Variance398.73851
MonotonicityNot monotonic
2023-07-16T09:23:39.537267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 71
19.4%
20 34
9.3%
24 30
 
8.2%
12 27
 
7.4%
16 26
 
7.1%
30 26
 
7.1%
40 21
 
5.7%
36 20
 
5.5%
28 16
 
4.4%
32 12
 
3.3%
Other values (31) 83
22.7%
ValueCountFrequency (%)
0 71
19.4%
8 1
 
0.3%
9 2
 
0.5%
10 4
 
1.1%
12 27
 
7.4%
13 1
 
0.3%
14 4
 
1.1%
15 1
 
0.3%
16 26
 
7.1%
18 10
 
2.7%
ValueCountFrequency (%)
96 3
 
0.8%
90 2
 
0.5%
88 1
 
0.3%
84 2
 
0.5%
80 3
 
0.8%
72 2
 
0.5%
70 2
 
0.5%
68 3
 
0.8%
66 1
 
0.3%
60 10
2.7%

红细胞体积
Real number (ℝ)

Distinct55
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.173224
Minimum0
Maximum75
Zeros36
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-07-16T09:23:39.641291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q136
median43
Q350
95-th percentile65.75
Maximum75
Range75
Interquartile range (IQR)14

Descriptive statistics

Standard deviation17.092791
Coefficient of variation (CV)0.41514337
Kurtosis1.1714432
Mean41.173224
Median Absolute Deviation (MAD)7
Skewness-0.99353563
Sum15069.4
Variance292.1635
MonotonicityNot monotonic
2023-07-16T09:23:39.749322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
9.8%
37 22
 
6.0%
45 17
 
4.6%
44 17
 
4.6%
50 16
 
4.4%
43 16
 
4.4%
35 16
 
4.4%
40 14
 
3.8%
36 13
 
3.6%
47 13
 
3.6%
Other values (45) 186
50.8%
ValueCountFrequency (%)
0 36
9.8%
4 1
 
0.3%
6.4 1
 
0.3%
23 1
 
0.3%
24 1
 
0.3%
26 1
 
0.3%
27 1
 
0.3%
28 1
 
0.3%
30 3
 
0.8%
31 2
 
0.5%
ValueCountFrequency (%)
75 2
0.5%
74 2
0.5%
73 2
0.5%
72 1
 
0.3%
71 1
 
0.3%
70 1
 
0.3%
69 2
0.5%
68 4
1.1%
67 1
 
0.3%
66 3
0.8%

总蛋白值
Real number (ℝ)

Distinct85
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.796721
Minimum0
Maximum89
Zeros42
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-07-16T09:23:39.862347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.1
median7.15
Q353.75
95-th percentile74
Maximum89
Range89
Interquartile range (IQR)47.65

Descriptive statistics

Standard deviation27.142298
Coefficient of variation (CV)1.2452468
Kurtosis-0.52467259
Mean21.796721
Median Absolute Deviation (MAD)1.35
Skewness1.1383707
Sum7977.6
Variance736.70432
MonotonicityNot monotonic
2023-07-16T09:23:39.967558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
11.5%
7.5 17
 
4.6%
6.5 16
 
4.4%
7 15
 
4.1%
6.6 12
 
3.3%
6 11
 
3.0%
5.9 10
 
2.7%
65 10
 
2.7%
6.7 9
 
2.5%
6.8 9
 
2.5%
Other values (75) 215
58.7%
ValueCountFrequency (%)
0 42
11.5%
3.3 1
 
0.3%
3.5 1
 
0.3%
4 1
 
0.3%
4.5 2
 
0.5%
4.6 1
 
0.3%
4.7 1
 
0.3%
4.9 2
 
0.5%
5 1
 
0.3%
5.3 2
 
0.5%
ValueCountFrequency (%)
89 1
 
0.3%
86 1
 
0.3%
85 1
 
0.3%
82 1
 
0.3%
81 2
 
0.5%
80 2
 
0.5%
79 1
 
0.3%
77 2
 
0.5%
76 2
 
0.5%
75 5
1.4%

y
Categorical

Distinct2
Distinct (%)0.6%
Missing9
Missing (%)2.5%
Memory size3.0 KiB
1.0
219 
0.0
138 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1071
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 219
59.8%
0.0 138
37.7%
(Missing) 9
 
2.5%

Length

2023-07-16T09:23:40.062443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T09:23:40.160547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 219
61.3%
0.0 138
38.7%

Most occurring characters

ValueCountFrequency (%)
0 495
46.2%
. 357
33.3%
1 219
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 714
66.7%
Other Punctuation 357
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 495
69.3%
1 219
30.7%
Other Punctuation
ValueCountFrequency (%)
. 357
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1071
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 495
46.2%
. 357
33.3%
1 219
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 495
46.2%
. 357
33.3%
1 219
20.4%

Interactions

2023-07-16T09:23:38.171037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:36.223619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:36.719291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.225491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.699866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:38.263082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:36.334695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:36.822359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.324636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.802894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:38.460725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:36.435720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:36.919420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.420721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.904979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:38.542742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:36.532249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.022444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.508824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.994998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:38.633528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:36.629273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.135473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:37.601846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-16T09:23:38.086018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-07-16T09:23:40.226099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
直肠温度脉搏呼吸频率红细胞体积总蛋白值y
直肠温度1.0000.1520.1780.0390.1000.211
脉搏0.1521.0000.3370.3810.0120.436
呼吸频率0.1780.3371.0000.1390.0150.102
红细胞体积0.0390.3810.1391.0000.3320.387
总蛋白值0.1000.0120.0150.3321.0000.098
y0.2110.4360.1020.3870.0981.000

Missing values

2023-07-16T09:23:38.756164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-16T09:23:38.874712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

直肠温度脉搏呼吸频率红细胞体积总蛋白值y
038.5662845.08.40.0
139.2882050.085.00.0
238.3402433.06.71.0
339.11648448.07.20.0
437.31043574.07.40.0
50.0000.00.01.0
637.9481637.07.01.0
70.060044.08.30.0
80.0803638.06.20.0
938.390040.06.21.0
直肠温度脉搏呼吸频率红细胞体积总蛋白值y
35636.6481627.056.00.0
35738.590047.079.0NaN
3580.0751258.08.5NaN
35938.242035.05.9NaN
36038.2786059.05.8NaN
36138.6603040.06.0NaN
36237.8424036.06.2NaN
36338.0601244.065.0NaN
36438.0421237.05.8NaN
36537.6883644.06.0NaN

Duplicate rows

Most frequently occurring

直肠温度脉搏呼吸频率红细胞体积总蛋白值y# duplicates
10.0000.00.01.04
00.0000.00.00.03
20.00046.05.90.02
30.0701660.07.50.02
40.096060.00.00.02
50.01003052.06.61.02
637.5483048.08.61.02
737.8482846.05.91.02
838.002468.07.80.02
938.1522454.07.50.02
\ No newline at end of file diff --git a/水稻叶片病害分类/平时作业/病马数据集/病马数据集.docx b/水稻叶片病害分类/平时作业/病马数据集/病马数据集.docx new file mode 100644 index 0000000..07d0bd0 Binary files /dev/null and b/水稻叶片病害分类/平时作业/病马数据集/病马数据集.docx differ diff --git a/水稻叶片病害分类/平时作业/病马数据集/病马数据集.ipynb b/水稻叶片病害分类/平时作业/病马数据集/病马数据集.ipynb new file mode 100644 index 0000000..905cd95 --- /dev/null +++ b/水稻叶片病害分类/平时作业/病马数据集/病马数据集.ipynb @@ -0,0 +1,2205 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score,confusion_matrix,roc_curve,roc_auc_score\n", + "from sklearn.ensemble import RandomForestClassifier,BaggingClassifier,AdaBoostClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "# 中文正常显示\n", + "plt.rcParams['font.sans-serif'] = ['SimHei']\n", + "plt.rcParams['axes.unicode_minus'] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "outputs": [ + { + "data": { + "text/plain": " 直肠温度 脉搏 呼吸频率 红细胞体积 总蛋白值 y\n0 38.5 66 28 45.0 8.4 0.0\n1 39.2 88 20 50.0 85.0 0.0\n2 38.3 40 24 33.0 6.7 1.0\n3 39.1 164 84 48.0 7.2 0.0\n4 37.3 104 35 74.0 7.4 0.0", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
直肠温度脉搏呼吸频率红细胞体积总蛋白值y
038.5662845.08.40.0
139.2882050.085.00.0
238.3402433.06.71.0
339.11648448.07.20.0
437.31043574.07.40.0
\n
" + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"data_horse.csv\",encoding=\"gbk\")\n", + "df.head()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "Summarize dataset: 0%| | 0/19 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
直肠温度脉搏红细胞体积总蛋白值y
038.56645.08.40.0
139.28850.085.00.0
238.34033.06.71.0
339.116448.07.20.0
437.310474.07.40.0
\n" + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 剔除相关性绝对值小于0.1的变量\n", + "df = df[df.columns[df.corr()['y'].abs()>0.1]]\n", + "df.head()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "直肠温度 0\n脉搏 0\n红细胞体积 0\n总蛋白值 0\ny 9\ndtype: int64" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 36, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" 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\n" 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\n" 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\n" 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TvCLH3lu+ltl7yw1jjHsmHI1TmPACAGjichIAAE2EFwBAE+HF0qoqJ9YD266qrpl6Bk5dPtXIUjjJBVTfO+VMwNL67NQDcOpycj2nPBdQBeBUYceLZeACqsC2q6qPJzkjs//QHbUryeYY49JppuJUJ7xYBi6gCizClUnemeSqMcbhR/hZ2BKHGlkKLqAKLEJVHUjy7THGkalnYTkILwCAJi4nAQDQRHgBADQRXgAATYQXAEAT4QWsnKp6TVW9dP74D6rqJVPPBKwG4QWsohuSvHT++PlJbp5uFGCVCC9g5Ywx/jPJ/qo6mOQzY4wHJh4JWBHCC1hV70lyXWa7XwAthBewqm5KspnktqkHAVaH8AJWTlU9LckHk7xujOH2HUAbtwwCAGhixwsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaPL/BcvCqIdAqlIAAAAASUVORK5CYII=\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#可视化每一个变量\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "for i in df.columns:\n", + " plt.figure(figsize=(10,5))\n", + " plt.title(i)\n", + " # 设置字体旋转角度\n", + " plt.xticks(rotation=90)\n", + " sns.countplot(df[i])\n", + " plt.show()\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 39, + "outputs": [ + { + "data": { + "text/plain": "直肠温度 68\n脉搏 26\n红细胞体积 36\n总蛋白值 42\ny 0\ndtype: int64" + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 我们认为这些变量中的0值都是不合理的，所以我们将除了y(最后一列)以外的变量中的0值都替换为np.nan\n", + "df[df.columns[:-1]] = df[df.columns[:-1]].replace(0,np.nan)\n", + "df.isnull().sum()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [ + { + "data": { + "text/plain": "直肠温度 0\n脉搏 0\n红细胞体积 0\n总蛋白值 0\ny 0\ndtype: int64" + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 用众数填充缺失值\n", + "# 观察发现直肠温度列是正太分布的，因此可以用均值填充缺失值，其他使用中位数\n", + "df['直肠温度'] = df['直肠温度'].fillna(df['直肠温度'].mean())\n", + "df[['脉搏','红细胞体积','总蛋白值']] = df[['脉搏','红细胞体积','总蛋白值']].fillna(df[['脉搏','红细胞体积','总蛋白值']].median())\n", + "df.isnull().sum()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [], + "source": [ + "## 删除缺失值\n", + "df = df.dropna()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" 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\n" 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\n" 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\n" 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TvCLH3lu+ltl7yw1jjHsmHI1TmPACAGjichIAAE2EFwBAE+HF0qoqJ9YD266qrpl6Bk5dPtXIUjjJBVTfO+VMwNL67NQDcOpycj2nPBdQBeBUYceLZeACqsC2q6qPJzkjs//QHbUryeYY49JppuJUJ7xYBi6gCizClUnemeSqMcbhR/hZ2BKHGlkKLqAKLEJVHUjy7THGkalnYTkILwCAJi4nAQDQRHgBADQRXgAATYQXAEAT4QWsnKp6TVW9dP74D6rqJVPPBKwG4QWsohuSvHT++PlJbp5uFGCVCC9g5Ywx/jPJ/qo6mOQzY4wHJh4JWBHCC1hV70lyXWa7XwAthBewqm5KspnktqkHAVaH8AJWTlU9LckHk7xujOH2HUAbtwwCAGhixwsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaPL/BcvCqIdAqlIAAAAASUVORK5CYII=\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 再查看一下每个变量的分布\n", + "for i in df.columns:\n", + " plt.figure(figsize=(10,5))\n", + " plt.title(i)\n", + " # 设置字体旋转角度\n", + " plt.xticks(rotation=90)\n", + " sns.countplot(df[i])\n", + " plt.show()\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 46, + "outputs": [], + "source": [ + "# 由于变量的取值范围不一样，因此需要对数据进行标准化\n", + "import time\n", + "X_train,X_test,y_train,y_test = train_test_split(df[df.columns[:-1]],df['y'],test_size=0.3,random_state=0)\n", + "X_train = StandardScaler().fit_transform(X_train)\n", + "X_test = StandardScaler().fit_transform(X_test)\n", + "\n", + "model_result = {}\n", + "\n", + "models = [RandomForestClassifier(),BaggingClassifier(),AdaBoostClassifier(),GaussianNB(),LogisticRegression(),DecisionTreeClassifier(),SVC(),KNeighborsClassifier()]\n", + "for model in models:\n", + " try:\n", + " model_name = str(model).split('(')[0]\n", + " start = time.time()\n", + " model.fit(X_train,y_train)\n", + " y_pred = model.predict(X_test)\n", + " end = time.time()\n", + " # 存储准确率和混淆矩阵\n", + " model_result[model_name] = [accuracy_score(y_test,y_pred),confusion_matrix(y_test,y_pred),end-start]\n", + " except Exception as e:\n", + " print(model_name,e)\n", + "\n", + "## 使用df保存模型的结果\n", + "df_result = pd.DataFrame(model_result).T\n", + "df_result.columns = ['accuracy_score','confusion_matrix','time']\n", + "\n", + "df_result.sort_values(by='accuracy_score',ascending=False,inplace=True)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 47, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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9jXhJUWm7ZjNa1WMKtq5Tr2PGPLj8ucMPP5w///nK5febN2+6vF19LVi8kLkJT8HWdfoVoLy8nGeffZqmTZvSpEkprVq1ynu8IiJpUuIlRaVV85bscMMO9TrGsh2X8Y9p/6DZzs045uljoCk8fMPDKzdqBv/wf3CRX1T9ANuxcgxV79fD+FPHMzfhKdi6Tr8ClJSUcOaZZ/PXv97Myy+/yC9+sXsiMSelLrVvJ510On/60zmUlS2lVavWjBgxkkWLFlV7rFmzuu/YISLp0VSjyGpq0rIJLbq3oEkb/flA3adf7733Tp544j8AzJ07h7ZtG1f9W12XHnnqqSc46KBDuO66Uay99tq8+urLsY+JSGHS/xwiUi877TSAJ598nBtuuIaxY59m44034bbbRlVr99prE9h6622W399338E8+eTjnHzycZSVLWO77fomGXbe1bX2bfDgA+jTJ/TFzJk/0r79WrGPSfD999/z+uuvMn/+vLRDEcmJphpFhA5tm9G0Vd1q3zp1ascDD9zH+PHjOfXUk+jUqRP9+vWu1u688/5Q7efuu++e2GM+9NADdYolztIFC/lxbuEsPVLhvffeYc6cOfTsuWWtjxWiukzBnnXWudWWGpk6dQrXXnslvXv34eabr+fWW+/UFKxknhIvEaFpq5a8sNPO9TpGG+D9hgmnQe087gVIIfGqT+3b7NmzuPbaq7j00itrfawQ1XX5kbidHj777BPOOedPrL/+BnzyyVS++eYrunb9SZJvR2S1aapRRCQP6lr7tmTJEs4//4+ccMLJdO68Xo2PFaq6TsHGLTWy6657sO66nXn55ZeYM2cO66+/Yb7DF6k3jXiJiMRYc41WNG9R91NkXZceuf/++5ky5UMeeOBuHnjgbg4++GBmzpxZ7bG99967Xu9v8aKlzJqd/EK1dZ2CrWmnhwULFjB27NO0a7cGJSVaa1CyT4mXiEiM5i2acuOZ/1evY+y00aFMe+cjBmxyGM/8dQrN6FbtmM3pzkdfLeKjJyseb8eRO12w/PlPni2LfezGZ+sX2yl/3qdeP19X9ZmCjdOuXTvOO+8iLr74fD744H169OhZa3uRtGmqUUQkT1o2b033LlvTpuUaaYeSGXWdgo1z9dUjeeutSQDMmTOXdu0az/6n0nhpxEtERHK2ZrvmNG/Zos4/X9cp2AqVd3o45ZQTOeussygpKWGHHXagd+/6X+25eOEiZs1ZXO/jiNREiZeIiOSsecsWXHrY/qtuWIutW5fyyYvP0nuNNvzjkvMAuPSw8dXavT7pZV6/+7aVHtsMVnr9nqXh+8xXnuPSV56rV1wA5977MCjxkjzKS+JlZqOBzYHH3f2SmOdPBA6M7rYHXnX34/MRi4iIZEuLpqX8ZK32aYchkooGr/Eys8FAqbv3A7qYWfeqbdz9Zncf4O4DgBeB26q2EREREWls8jHiNQAYE90eC/QHpsQ1NLP1gXXdfWJtBywtLaF9+9YNGWNBKeb3Xhv1Szz1S3Xqk3jql3jqF8mnfCRebYCvotuzgW61tD0ZuHlVBywrK2fmzPmxz1UtvGyManrvtVG/xFO/xGvs/aI+iad+iVeXfhGprLa/k3wsJzEXaBXdblvTa5hZE2AXd69/NaSIiIhIAchH4jWRML0I0Av4rIZ2OwKv5uH1RURERDIpH4nXI8DhZnYNMASYbGbVrmwE9gDG5eH1RURERDKpwWu83H22mQ0AdgOudPfpwNsx7c5p6NcWERERybK8rOPl7j+y4spGEREREUF7NYqIiIgkRomXiIiISEKUeImIiIgkRImXiIiISEKUeImIiIgkRImXiIiISEKUeImIiIgkRImXiIiISEKUeImIiIgkRImXiIiISEKUeImIiIgkRImXiIiISEKUeImIiIgkRImXiIiISEKUeImIiIgkRImXiIiISEKUeImIiIgkRImXiIiISEKUeImIiIgkRImXiIiISEKUeImIiIgkRImXiIiISEKUeImIiIgkRImXiIiISELykniZ2Wgze9nMzltFu1Fmtk8+YhARERHJmgZPvMxsMFDq7v2ALmbWvYZ2OwKd3f3/GjoGERGRYrZgwQLeeOM1vv32f2mHIlXkY8RrADAmuj0W6F+1gZk1A/4KfGZm++UhBhERkYIycuQITjjhaO6882+xzy9dupTBg3/JKacM45RThvHxx1NZsmQJ55//B045ZRjnnPN7li5dysKFCznzzFN57713OPvs4XzyyccJvxOpTdM8HLMN8FV0ezbQLabNEcD7wJXAqWbW1d1vqOmApaUltG/fusEDLRTF/N5ro36Jp36pTn0ST/0SL41+efrpp2natAkPPvggF188glmzvmWjjX6yUpv333+fX/3qV5xxxpnLH3vuubFsuWUPhg07nr/85TomTXqFrl03YtiwYQwYMIB11lmbqVPfZ9ttt0z4HUlN8pF4zQVaRbfbEj+qtg1wm7tPN7N7gUuBGhOvsrJyZs6cH/tcp07t6hdtAajpvddG/RJP/RKvsfeL+iSe+iVeXfqlvsaPf5n+/Qcwc+Z8evbcmpdemsCaa66zUpsJE17nqaee5vXX36Bz5/U499wLKS1tydSpn/D119/z4YdOv34D6Ny5K507d+W11ybx5JNPcfbZ56XynopZbX8n+ZhqnMiK6cVewGcxbaYCm0S3fwZ8noc4RERECsKCBQvo2DEkWm3atGXGjBnV2my++RbceONt3HzzaNq2bceECePZeONNWbJkCQ8//CAtW7Zi/fXXX95+/PgXWbBgAS1atEjsfciq5SPxegQ43MyuAYYAk83skiptRgO7mNk44CTg6jzEISIiUhBatWrNokWLAFiwYD7l5cuqtdl00+507NgRgI022ohp06Zx//13c+ihR3LEEUczYMBA7rnnzuXtjzrqOPbd99c88cR/EnkPkpsGT7zcfTahwH4CsIu7v+3u51VpM8fdD3D3ndx9e3f/Ku5YIiIixcBsM9555y0Apk6dQufOXaq1ufjiC5gy5SPKysoYN+55unXrzqJFC/nkk6kAvPvuOwA8++xTywv058yZS9u2jX96uJDko8YLd/+RFVc2ioiINGod1mxF0+Z1/y918OB9OOSQQ5g/fxbjxo3j2muv5d57/8bw4cOXtznjjNM588xQWD9w4ED23ntXevb8KWeccQZXXXUZ3bt358Ybb6RDhw6ceeaZ/Pa3J7DOOutw+eWX13u6cenipfw4a0G9jiFBXhIvERGRYtK0eVM+uHRsvY5x/tbH8dYnH3J+72GU/+Nr9qRXtWNe2TdKxOaz/LmLtzkpXLIGzPjbZGYAJ3UeBJ3DY59cPb5ecQFsfu7Aeh+jLkaOHMHnn39G3779GDr02GrPL126lCFD9qNLl1DbNnz4WWy6aVhM4b777qJFixbsv/9BABx11CG0adMWgCOPPJo+ffom9C5WpsRLREQkA9q2aEP/n/ROO4zMeOGFsSxbtoxbbrmdq6++nGnTvmDDDbuu1Objj6ey6657cNJJp630+JdfTuOll8Zx4423ATBr1ky6dt2Iiy4amVj8NdFejSIiIpI5b745kYEDdwWgd++fLa+Bq2zy5HcZN+55TjzxGC666DyWLl0KwFVXXcaGG3blmWeepKysjPfff493332Hk046lt///nTmzZub5FtZiRIvERERyZy6LrHxxhuvsXDhQo499gQWLJjPqFHX06XLBvzlLzczatTf2Gab3jz+eHpXeirxEhERkcyp6xIbH33k7LXXL1lnnXXZa69f8eabb9Cly/pssMGGAHTt+hO+/PKL5N5IFUq8REREJHPqusTGBhtsyNdfh1WqPvzwA9Zddz1uu20U48ePA+C5556hW7efJvY+qlJxvYiIiOTFmmu2oHnz5nX62bousVFWVsabb77Kb397AvPmzeOKK66gQ4cOnHzyyYwefQtbb701hx9+EM2aNavXe1u8eDGzZi1a7Z9T4iUiIiJ50bx5cy688MI6/3zPnj2ZMmUKW221FQ8++CBAteP17h2uBJ01a9by55o2bUr37t0BeOCBBwDo0aPH8p+59NJL6xxThfBaSrxERESkkWjevDldu3ZddcMCohovERERkYQo8RIRERFJiBIvERERkYQo8RIRERFJiBIvERERkYQo8RIRERFJiBIvERERkYQo8RIRERFJiBIvERERkYQo8RIRERFJiBIvERERkYQo8RIRERFJiBIvERERkYQo8RIRERFJiBIvERERkYQo8RIRERFJSNN8HNTMRgObA4+7+yUxzzcFPom+AE5193fzEYuIiIhIVjT4iJeZDQZK3b0f0MXMusc02wp4wN0HRF9KukRERKTRy3nEy8x6AusDXwDT3H1uDU0HAGOi22OB/sCUKm36AoPMbAfgc+BId1+6GnGLiIiIFJycEi8zuwHoAmwMnA9cAexbQ/M2wFfR7dlAt5g2rwM7u/s3ZnYTsDfw75pev7S0hPbtW+cSaqNUzO+9NuqXeOqX6tQn8dQv8dQv8dQv1dWlT3Id8drS3QeY2Vh3f8zMzqql7VygVXS7LfHTme+4+6Lo9odA3HTkcmVl5cycOT/2uU6d2tUeeSNQ03uvjfolnvolXmPvF/VJPPVLPPVLPPVLdXXJTXKt8frOzC4AOpjZkcD0WtpOJEwvAvQCPotpc4+Z9TKzUmAQ8HaOcYiIiIgUrFwTryOAWcArwJrAUbW0fQQ43MyuAYYAk82s6pWNI4B7gLeAV9z9mdWIWURERKQg5TTV6O4LgL/k2Ha2mQ0AdgOudPfpVBnRcvf3CFc2ioiIiBSNXIvrn3D3vXI9qLv/yIorG0VERESE3Kca3zWz/fIaiYiIiEgjl+tVjX2AU83sXWAeUO7uA/MXloiIiEjjk2uN1y75DkRERESkscu1xqspcDRh/8XJwJ1aaV5ERERk9eRa43UH0Bn4L2HboDvyFpGIiIhII5VrjdcG7n54dPtJM3s+T/GIiIiINFq5Jl7fmNkfgVeB7YGv8xeSiIiISOOU61TjUMKG178BZkT3RURERGQ15Jp4NQFedfeTgcV5jEdERESk0co18RoD9Ihurwvcl59wRERERBqvXBOvDu5+F4C7XwZ0zF9IIiIiIo1TrsX1X5rZ2cBrwHbAt/kLSURERKRxWp3i+vnA/oQtg47IV0AiIiIijVWuI15LgLuABUB/oDmwKF9BiYiIiDRGq1Nc3xe4BjgW+FfeIhIRERFppHJNvDq6+1NAd3c/FGiVx5hEREREGqVcE685ZvYIMNHM9gbm5C8kERERkcYp1xqvA4At3H2SmfUCDsxjTCIiIiKNUk6Jl7svBCZFt9+ueNzM/uXug/IUm4iIiEijkutUY03aN0QQIiIiIsWgvolXeYNEISIiIlIE6pt4iYiIiEiO6pt4lTRIFCIiIiJFIKfEy8yGmFnzqo+7+y4NH5KIiIhI45TriNdmwPNmdquZ7ZDPgEREREQaq5wSL3cf4e79gPuBu81sipkNram9mY02s5fN7Lzajmtm65rZm6sVsYiIiEiBynWq8cBo5foLgCsI+zaeWEPbwUBplKh1MbPutRz6arT9kIiIiBSJXFeu3xw4w90/qXjAzI6qoe0AwqbaAGOB/sCUqo3MbCAwD5i+qhcvLS2hffvWOYba+BTze6+N+iWe+qU69Uk89Us89Us89Ut1demTXBOvK4AewCdmdgxwj7u/X0PbNsBX0e3ZQLeqDaJC/QuAXwOPrOrFy8rKmTlzfuxznTq1W9WPF7ya3ntt1C/x1C/xGnu/qE/iqV/iqV/iqV+qq0tukmtx/UOExAtgXeC+WtrOZcX0YdsaXuMPwE3uPjPH1xcREREpeLkmXh3c/S4Ad78M6FhL24mE6UWAXsBnMW12BU42s+eBrc3sbznGISIiIlKwcp1q/NLMzgZeA/oA39bS9hHgRTPrAuwFHGRml7j78isc3X2nittm9ry7H7vakYuIiIgUmFxHvIYC84H9gQXAETU1dPfZhAL7CcAu7v525aQrpv2AHGMQERERKWg5jXi5+yIze5AVtVvbAq/U0v5HVlzZKCIiIiLkmHiZ2WhgY6ADYeSrnBV1XCIiIiKSg1ynGjcC9gSmAjsDy/IWkYiIiEgjlWvitQj4BVAKHEAY+RIRERGR1ZBr4jWEsPr8cMIq9iflLSIRERGRRirX4vp5hGlGCCvOi4iIiMhqynWT7CfyHYiIiIhIY5frVOO7ZrZfXiMRERERaeRyXbm+D3Cqmb0LzAPK3X1g/sISERERaXxyrfHaJd+BiIiIiDR2uS6gWm2LIHe/u+HDEREREWm8cq3xKom+WgODgZ1qby4iIiIiVeU61XhXpbu3mNmoPMUjIiIi0mjlOtVYeYRrDaBHfsIRERERabxyvaqxcnH9IrRyvYiIiMhqy7XG60rgMXe/CPiOsH2QiIiIiKyGXBOvh1gxvbgucF9+whERERFpvHJNvDpUFNi7+2VAx/yFJCIiItI45Vrj9aWZnQ28BmwHfJu/kEREREQap1xHvIYC84H9CVsGVVtQVURERERqtzoLqL7i7icDC4Dy/IUkIiIi0jjlmniNQcX1IiIiIvWi4noRERGRhKi4XkRERCQhq1Nc3wI4k1Bcf119X9jM1jKz3cxMo2ciIiJSFHJNvEYBA4ANgIOBq2prbGajzexlMzuvhufXAx4jjJ49Z2adco5YREREpEDlmnhtBOwJfATsDCyrqaGZDQZK3b0f0MXMusc06wEMd/dLgSeBbVcrahEREZEClGuN1yLgF1H7A4AOtbQdQLgKEmAs0J8qezu6+zMAZrYTYdRrRM4Ri4iIiBSoXBOvIcB6wHDgGOCkWtq2Ab6Kbs8GusU1MrMS4EBgCVBW24uXlpbQvn3rHENtfIr5vddG/RJP/VKd+iSe+iWe+iWe+qW6uvRJTomXu88DpkZ3L1hF87lAq+h2W2qYznT3cuBkM7sY+BVhI+5YZWXlzJw5P/a5Tp3arSKcwlfTe6+N+iWe+iVeY+8X9Uk89Us89Us89Ut1dclNcq3xWh0TCdOLAL2Az6o2MLOzzaxi26H2wMw8xCEiIiKSKflIvB4BDjezawhTlJPN7JIqbW6L2owDSoGn8hCHiIiISKbkWuOVM3efbWYDgN2AK919OvB2lTY/Rs+LiIiIFI0GT7xgeWI1ZpUNRURERIpIPqYaRURERCSGEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUmIEi8RERGRhCjxEhEREUlI03wc1MxGA5sDj7v7JTHPrwk8GL3+XOBAd1+cj1hEREREsqLBR7zMbDBQ6u79gC5m1j2m2aHANe6+GzAd2LOh4xARERHJmnyMeA0AxkS3xwL9gSmVG7j7qEp3OwHf5iEOERERkUzJR+LVBvgquj0b6FZTQzPbHujg7hNqO2BpaQnt27duuAgLTDG/99qoX+KpX6pTn8RTv8RTv8RTv1RXlz7JR+I1F2gV3W5LDdOZZrYWcAPwm1UdsKysnJkz58c+16lTu7pFWUBqeu+1Ub/EU7/Ea+z9oj6Jp36Jp36Jp36pri65ST6uapxImF4E6AV8VrWBmTUnTEf+0d0/z0MMIiIiIpmTj8TrEeBwM7sGGAJMNrOqVzYeA/QGzjWz583swDzEISIiIpIpDT7V6O6zzWwAsBtwpbtPB96u0uZm4OaGfm0RERGRLMvLOl7u/iMrrmwUEREREbRyvYiIiEhilHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhC8pJ4mdloM3vZzM6rpc26ZvZiPl5fREREJIsaPPEys8FAqbv3A7qYWfeYNh2Au4A2Df36IiIiIlnVNA/HHACMiW6PBfoDU6q0KQMOBB7N5YClpSW0b9+6oeIrOMX83mujfomnfqlOfRJP/RJP/RJP/VJdXfokH4lXG+Cr6PZsoFvVBu4+G8DMcjpgWVk5M2fOj32uU6d2dQqykNT03mujfomnfonX2PtFfRJP/RJP/RJP/VJdXXKTfNR4zQVaRbfb5uk1RERERApOPpKiiYTpRYBewGd5eA0RERGRgpOPxOsR4HAzuwYYAkw2s0vy8DoiIiIiBaXBa7zcfbaZDQB2A6509+nA2zW0HdDQry8iIiKSVfkorsfdf2TFlY0iIiIiggrfRURERBKjxEtEREQkIUq8RERERBKixEtEREQkIUq8RERERBKixEtEREQkIUq8RERERBKixEtEREQkIUq8RERERBKixEtEREQkIUq8RERERBKixEtEREQkIUq8RERERBKixEtEREQkIUq8RERERBKixEtEREQkIUq8RERERBKixEtEREQkIUq8RERERBKixEtEREQkIUq8RERERBKixEtEREQkIUq8RERERBKixEtEREQkIU3zcVAzGw1sDjzu7pfUtY2IiIhIY9LgI15mNhgodfd+QBcz616XNiIiIiKNTT6mGgcAY6LbY4H+dWwjIiIi0qiUlJeXN+gBoynE6939bTPbHdjW3S9f3TZVfAd83qCBioiIiOTHRkCnuCfyUeM1F2gV3W5L/KhaLm0qiw1eREREpJDkY6pxIiumDnsBn9WxjYiIiEijko+pxjWAF4Fngb2Ag4AD3P28Wtr0dfdZDRqIiIiISMY0eOIFYGYdgN2Ace4+va5tRERERBqTvCReIiIiIlKdVq4XERERSYgSLxEREZGEKPESERERSUhe9mpsTMysBNgdmAe8AvwBaAfc5O7T0owtbWbWAtgGaF7xmLuPSy8ikcJhZq2BYcAUd3/MzH4HzAfucPcF6UYnIvmiEa9Vux/4NXA68ALQkXByvD/FmLLiWeAwYJfoa0Cq0WSEmT2RdgxZpH6p5h5gIfB+dP8FoDXwQGoRZYSZXZx2DFkU7foiVRTauUUjXqu2rrsfDGBm77j78Oj27umGlQnL3P2UtIPIoHfNbD93fzTtQDJG/bKyzu5+S8Udd38deN3MBqUYU1b0MLON3f3TtAPJmBIz6xP9rsgKBXVuUeK1au3NbHvC6ODSSrdb1f5jReFpM7scuIswFYu7f5FuSJnQBzjVzN4l9Eu5uw9MOaYsUL+sbKyZjQUeB2YQtk/bDXgj1aiy4TNgnJk9QNhiDncfkWpE2dAceMbMnmTF39DRKceUBQV1btE6XqtgZncAFZ1UEt0uQb/wFX0D6hOROok+yO0BrAvMBsa7+7/TjSp9ZrZz1cfc/YU0YskSM9uo6mPu/nkasUjdKfFaBTP7U5WHlneYPoGBme0BbA685+7PpB1PVphZT2B94AtgmrvPTTmkTFC/rGBmtwP/BZ529x/TjieLzGxNYLEuNlgh2vWlC2GU9H/uvizlkDKhkM4tKq5fPeWEIvILUSE5ZnYNMARYABxqZn9OOaRMMLMbgIuAkcAm6EIMQP0S419AP+B5M3vZzC40s77RldRFzcwOM7P3CFeSH2VmV6UdUxaY2dnAE4QLMAYCd6YaUEYU2rlFidcquHvFP+Y04JeEbHrbLM8fJ6i3ux/j7re6+1GEeXaBLd39N8BMd38MWDPtgDJC/VKJu/+fu//W3XsBBwBrAeOA79ONLBNOJixV8z93HwXskHI8WbGPu/cFfnD3+whJhhTYuUXF9atgZpcBxwDvAVcA04HWZtbP3V9ONbj0zTazg4FXgb7ArJTjyYrvzOwCoIOZHUn4nRH1y0rMrBuwa/S1NTAROAF4MsWwsmIBsD0sr2ual244mTHbzI4AWkZ1cDNTjicrCurcohGvVVuPcNXRF8CvgGOB46Lvxe5IYFvgBqAXcES64WTGEYQk9BXCJ6+j0g0nM9QvK7uXMIr+Z+CnwGNAKfBDmkFlxPHAcGAd4FpAy9YEQwkjgT8C+xEGBaTAzi0qrs+Rma1LmFOvWKW93N3vTjEkESlgZnY3sNDdh5nZdUAnYDLQx921lpdII6URr9w9QbhiQkSkIWwYJV3dCB/qDnP3y8h4fYqI1I9qvHI3292vTjuILDCzs9z9yrg1zop5HS8zu8bdzzCz56jeL0V7MYb6pUY/RvszDgIuBtqY2eCUY0qVmR3h7ndHy/isNB1TzMv36Jwbr1DPLUq8cvdStIry3axYpb1YN4S+K/p+YZpBZI27nxF93yXtWLJE/VKjw4DDgZHu/p9o5GsL4NB0w0rV29H359MMIoN0zo1RqOcW1XjlKG4h1WL+BCYiIiKrTzVeOYrW8xoF3BF9PZ1uRNliZhubWVH/PpnZftH3tdOOJUvUL5KruC1xBMzs52nHkEWFem4p6v8oV4eZjQYeAh4lrBpc9Cspm9nNZvYbM7sIuAcYk3ZMKTs9+v73VKPIHvWL5OpOADO7J+U4smYkgJnpA//KCvLcohqv3G0E7AncBxwMjE03nEzo4e4nmtkwd+9vZsW+oGy5mY0ANo4W81uuyKel1S+SqxZmNhDoYWY7VX6iiGtqAZqb2dHABtECqssV+bJGBXluUeKVu0XALwgLHB4AdEg3nExYGq0/NMXMtgOWpBxP2gYRFpLdh1AcXPR77kXUL5Kr0wkfcDsQ9sOt+F0pJ2ynVKwOBSonovobCgry3KLi+hyZWRvCKvZLCKsFP+3uL6YbVbrMrBOwI2GNs+2Bj93983SjSp+Znebu16cdR9aoXyRXZnZ7MS+TUBMzu8zdz0k7jqwptHOLEi9pEGa2MfC5uy9LOxYREZGs0lSj1JmZ3Qw8A2xFmIadDuyfalAiIiIZpsRrFQp1ZdyEqLi+Ev2uxFO/SK60cn28GlauB6CYp2QL9dyiqUapMzMbC7xDSODvBq5y953TjUpECpWZ9XL3t82s2nnE3V9II6YsMLN13f1/ceucqa628CjxkjpTcb2IiMjqUeKVo2hV9rbAfEKy8Ya7z0k3qvSZWQegCzAD+J+K6/W7UhP1i9SFmZW6e1nacWSNLmhaodDOLarxyt0Y4DbCeiFrAecCu6YaUcrM7GzCOiqtgSuAPYAjav2h4qDflXjqF8mJmZ0LTAE6AmeZ2WPufnLKYaVOFzTVqKDOLdoyKHcd3f0poLu7Hwq0SjugDNjH3fsCP7j7fcAmaQeUEfpdiad+kVzt7e5jgP0I55XeKceTFT3c/R9AX3fvT5htkAI7tyjxyt0cM3sEmGhmewOZHcZM0Oxo+4qWUTHszJTjyQr9rsRTv0iulpnZb4GvgY0BTTUG2i0kXkGdW5R45e4AYIS7nwt8BRyYcjxZMBTYBviR8Mn0mFSjyQ79rsRTv0iujiX8/3QWsC1wWrrhZMaBhK2Tfk+oaVJpR1BQ5xbVeOVuMTDVzJoS5pA/STme1Ln7t8DwtOPIIP2uxFO/SE7c3QEHMLN/qrg+cPfvgH8CmNmnwLR0I8qMgjq3KPHKXUEV7yXBzEa7u0a5qtPvSjz1i+RExfXxVFxfo4I6t2iqMXcFVbyXkBIz65N2EBmk35V46hfJlYrr46m4Pl5BnVs04pW7gireS0hz4GkzewqYR9imoWi3r6hEvyvx1C+SKxXXx1NxfbyCOrco8crdAcAW7j7JzHqR8eK9hJwbfcnK9LsST/0iuToW+CWhuH4AKq6vcCAr7xai4vqgoM4tWrk+R9GyCStx97vTiCUrzOxod7+90v31gPbu/kGKYWVCtJ1SxXD3+u7+SprxZIX6RerCzJq7++K048gaM1vP3b9JO44sKKRzi0a8clcSfW8F7Al8T9gYupgNMLP9gOvc/TngcqADsG+6YaXLzEYTpkc6ELawKAf6pxpUBqhfJFdmdgJwFOH/qIqvHqkGlQFmdgmhgLxt9NA8QqF9USu0c4uK63Pk7ndFX7e4+68Jl68Wu58CJwIXRffXR8k8wEaE5HwqsDNQ9HupRdQvkqtDgb2A94DdgU/TDSczdgD6Aa8REq7v0g0nMwrq3KL/JHNkZjtVutsOffoCWAQcCbSN5tU3JvziF7tFhEu9Swm1Bx3SDScz1C+SqxKgM7A2YYBgg3TDyZRehBGvrYBOKceSFQV1btGIV+52qfS1FXBSuuFkwgHAB4QV7PcFTgVeTjOgjBhCWINoOLA5+l2poH6RXJ0ArAdcBzwE3JFqNNlxCGG25XzCbMMl6YaTGQV1blFx/WoopOK9pJhZB8JaMjOA/7l7pod4RURE0qSpxhwVWvFeEszsbGAQ0Bq4AtgDXd4sIiJSIyVeuaso3rsPOBgYm244mbCPu/c1s+fc/T4zOzHtgNJkZs8REvLKSggLyw5MIaRMUL9IrszsDmr+XSnaxZn1NxSvUPtFiVfuqhbvtU81mmyYHa1v1tLMdgZmphxPqtx9l7RjyCL1i6yGC9MOIIv0NxSvUPtFidcqRLudH0Ao9twXmEgY9bogzbgyYijwR+BHwp5qRfuJVETqz90/TzsGkXxT4rVqDxLWknkaOAz4EyHZ2JUivaLEzEoJ73+xuw+PHmsCDAYeTjO2NJlZa2AYMMXdHzOz3xHqAe9w9wXpRpce9YusDjPbmnBued/MBgDfufvkdKNKn5n9mtAX483sUOBHd3885bBSVajnFi0nsWrruvuF7v49cJO7P+XuVwBt0g4sRfcT9sI60cyuN7PTgbcp8osNgHuAhcD70f0XCBcePJBaRNmgfpGcmNkVwB+AdaOHNgOuMLOR6UWVvqj2rS9hpXqAucDeZnZXelFlQkGeWzTitWqTol3PnyfUNJ1OuHrvzTSDStmG7t7PzEoIK0qPAnZ095nphpW6zu5+S8Udd38deN3MBqUYUxaoXyRXP3f3ARV3ot+bW8zspfRCyoRN3f2oijvu/ijwqJmNSzGmLCjIc4sSr1Vw99PN7BfAAMKnr1nALe7+71QDS1dLM9uecPXIDOAlYAszw92LeQHVZ81sLPA4oV/aArsR6gKLWU398kaqUUkWfWdmfwIeY+XflR9TjSp9H0SjXlX75ctUo0pfQZ5ztYCqrLboBBCnqC/5BogS0j0IUyWzgfFFnqQD6hfJTVSzUzGrUPG78hJwmbv/kGZsaYpqaA+kyt8QcJu7L0wztrQV4rlFiZfUmZm1Anq4+xtmdixwt7tr8/AqzKy/uxf7VEnFfx7tCMWv/YE33H1OulFJITCzFu6+KO04ssDM1iJsp6TdQiJm9gd3v7zS/S2Aju6eyalYFddLfTzEis3C1yEss1H0zOzpKg8VdWFwJWOAnwPXAMcC/0o3HMkqM7uqykMvpBJIxkS7hTxOKB4fCNyZakDZsZWZTTCzg6L75wO/SzOg2qjGS+qjg7vfBeDul0WrCBctM9sK2AZYP1pYFsLVr0U9FVBJR3d/yszOcPc9zWx82gFJtpjZGoRt2XYws67Rw22ApelFlSnaLSTeJoRR9BcIS0CtQ9hMPJOUeEl9fBl9AnsN6AN8m3I8aSuJ+f4DMCSdcDJnTnSF8EQz2xvQNKNUtQvwa+AnhFXsSwhT05kdvUiYdguJNwO4idAv+wE/BTzdkGqmxEvqYyhh8br9gQ8p8g2y3f1t4G0zM3e/O+14MugAYAt3n2RmvQjFwiLLVVom4fFiv1CnBkNZebeQY1KNJjsGE1Yd+Iqwp/JehHXPMknF9SINLCoib0v4pL4jKiIXqTMzK3X3srTjyAoz6wB0ISRf01VcD2a2DqHmrTkrNsnO7IdfFdeLNLwxhE9b16Ii8uXM7Im0Y5DCYGbnmtkQMzsJ+NjMbko7piyISjueIBTXD0DF9RX+C2xA9XKPTFLiJavNzK6Jvj9nZmOjr+eihewkKiIHurv7oUCrtAPKiHej+guRVdnb3ccQptM2AXqnHE9W7OPufYEf3P1+Qt8IzHb3q939roqvtAOqjWq8ZLW5+xnR913SjiWjVEQerw9wqpm9S9hzrtzdB6Yck2TTMjP7LfA1sDGgqcZAxfXxXjKzB4C7ifazzOoaXqDESyQfVEQeQ4m6rIZjgV8CZxGm1E5LNZrsGIqK6+MsIVzgtV10vxzIbOKl4nppMFqhPTCzpsDRwObAZOBOdy/6dYjUL7K6zGxNYLG7L0g7FpGGohovqTOt0F6jO4DOhILP9aP7on6RHJnZ4Wb2HvAKcFTMSvZFycxGpx2D1J+mGmW1aYX2VdrA3Q+Pbj9pZs+nGUyGqF8kVycRzjFPufsoM3s57YAyosTM+rj762kHkgVmdo27nxHtmlIxfVexnERm60eVeEldaIX22n1jZn8EXgW2JxQIy8r90hf1i9RsAeFvBzPbiKhgWmgOPG1mT7HiApWiXWi2UC/0UuIlq00rtK/SUOA4wmrKk6P7sqJffgO8h/pFanY8cCVhz71rgVPSDSczzo2+pICpuF7qTCu0x4uKyI8hFJG/h4rIl4umqbsBn7r7m2nHI1IIzOzn7v5q2nFklZn9wd0vr3R/C8J6ipm8slHF9VIfWqE93h2ET+pPoCLy5czsz8AVwM+Ai83s+pRDkowys4vTjiFjdOFS7bYyswlmdlB0/3wyvLG6phqlPjq6+1Nmdoa772lm49MOKCNURB5vO3ffseKOmRX90iNSox5mtrG7f5p2IBmxdVTXVVlFEfnuaQSUMZsA/YEXgAcJH3wXpxpRLZR4SX1ohfZ4Kq6vxMy6Rje/MrPDCP3SG5ieXlSScZ8B46LVyOcCuPuIVCNKlxPqIyXeDOAmwor++wE/JfRZJinxkvrQCu3xhqLi+souir4vAH4BDCR8Wp+dWkSSdY9GXxI84+6fpx1Ehg0GNgO+AvYE9iKUwWSSiutFGoiZlQC7Ey7zfoWwtUdb4CZ3n5ZmbFlgZi0JazM1jx4qz2rxq6TPzIzwn+kH7v5R2vFkgZm1YOW/oUzvSZiUQiuu14iXSMO5n7BpbUfgcuB1wp5q9xOu+ix2zwBvAd9F9zO9n5qkx8zOJIxcvEHYWP1xd78m5bCy4Fn0NxRnKzObAFzn7g8SiuvbkNG+UeIlq61QVwtOwLrufjCAmb3j7sOj2yp+DZa5u9ZjklwMcvf+sHwk+SVAiZf+hmqi4npp3Ap1teAEtDez7QlJ6NLodhOgVbphpatScf3TZnY5cBfRSuTu/kVqgUmWLTSzvsBrhFodbUcW6G8onorrRYrU28CwmNvvpBNOZlxU5f5Z0fdyoGi3O5FaHQtcRViEeDJhQWIJIzsAvyeaZUB/Q6DieikWhVbQmCQz68SKka713f2VNOPJAjM7xt1HV7rfBVjT3T9IMSzJEDPr5+7aELsWZrYH0a4Y7v5M2vFkRSGdc7VyvdRHQa0WnBQzGw08RLgc/gHCJ3eBnc3sUTOrmKIeSVjJXqTCJRU3zOzYNAPJIjO7BhhCWJrl0Gg3iKJXaOdcJV5SHxUFjadG99cBmqUXTmZsRBjungrsDCxLN5zM+ClwIiumHtdH5Q6yspJKtw9JLYrs6u3ux7j7re5+FNAn7YAyoqDOuTrpSX0UVEFjghYRFgotJSwy2yHdcDJjEXAk0DZacHdjwolSpEJzM1uPMCjQIpqOBsDdi3oHiMhsMzuYsPtDX2BWyvFkRUGdc5V4SX0UVEFjgoYA6wHDCUXBJ6UbTmYcAPQjrOS/H2GkVJ/YpbLFwH2Vbt9b6bliXqqmwpGEhZkPA94Djkg3nHRVWrR6JPANMAnYmoyfc1VcL3VmZk0JicVmhCuP7nT3pelGlR4z26mm53TBgUjuzGwQsCWVph7dverVsVLkor08ZxIWrV6PFYtW7+bumV20WiNeUh93AFOA/xJGuu4ADk81onRVFI3vDCwlrLq9NdAOrVwvsjrOB84EytIORDKtIBetVuIl9bGBu1ckWk+a2QupRpOyik/kZvasuy//w49W+C9aZnaWu19pZndQfacDrUEkcT4CLgA+i+4X9dY4+huqUUEuWq3ES+rjGzP7IysKPb9MOZ6sWGZmpxEWTu1Bxq+wScBd0fcL0wxCCkoXQj3TMlYkGsVMf0PxCnLRatV4SZ2ZWXPgOGALQo1XqbvfkG5U6TOz9oQTQHfgc2CnyiNgIlI7M/uEkHB9XvFYke8DK42IEi9pMGb2mrtvl3YcaYkS0Z0IV3juSlhb5nrgOXd/PsXQMsPMOhBGM2YA/3P3Yh8NlBpohfbamdnGwOf6Gyo8mmoUaTg/AM0Ja5v9Avi7u/8p3ZCyw8zOBgYBrQkr1u9BkV8OL/GiFdrXJGySfaiZ7eXuZ6YcVurM7GbgGWArwjlmOrB/qkHJalPiJavNzOJWlC4B1ko6lozpSlhTZg/gJaCjmf0WGOvuma45SMg+7t7XzJ5z9/vM7MS0A5LM6u3uO0e3bzWzoi2sr6KHu59oZsPcvb+ZaV/LAqTES+qiew2P35NoFBnj7j8S9gt7CMDMtiQkYVcTErJiN9vMjiDsdLAzYf0dkThaoT3eUjO7DphiZtsBS1KOR+pANV4ikggzW4ew6rYBHwKXu/u36UYlWWRmaxF+V7YgrNB+efTBpqiZWSfCmoBPANsDH7v757X/lGSNEi8RyatolCuWu9+dZCySbWbWtcpDJUTLSbj7F8lHJNLwNNUoIvm2S6Xb5cAmhKs/PwaUeEllFdsCbU7Y8eEtQiH5IuBnKcUk0qA04iUiiTCzgcBpwBqEZTYedXedgKQaM3sa2MPdl5lZKfCUu/8i7bjSopXrGxeNeIlIXpnZccApwPfAtcCk6Kn1gK/TiksyrTXwSzN7l1Dn1TrleNKmlesbEY14iUhemdn3wLuVHioHmgHd3H29dKKSLDOznwBnARsT9mu82t0/TjMmkYbSJO0ARKTRmwbc4+67AAOB24GOwL9TjUqy7BvC8jSXAw8C66cbTnaY2UbR96Kdei10SrxEJN/6AwPM7GHgTWA34Jfufny6YUmGPQscCgyo9FX0ohqvA6K7B5rZrWnGI3WjGi8RybdewK3AUKANcB/Q2cw6u7tW3pY4y9z9lLSDyKCfuvtRAO4+zMyeSzsgWX1KvEQk344j1HWVELZSOjh6vBxQ4iVxnjazywlF5fNA63hFZpjZgYQ9LPsA81OOR+pAiZeI5FXFJ3SR1bBJ9P2s6Hs5oGUTwqjxOYTN5T8Ajkw1GqkTXdUoIiKZZmbrufs3aceRBWbWgXCxwQxgursvSzkkWU0a8RIRkUwxs4uBfYG2hNGueYRawaJmZmcDgwjrml0B7EEY/ZICoqsaRUQka/oD/Qi1TL0Ii+8K7OPufYEf3P0+VkzJSgFR4iUiIlnUizDitRXQKeVYsmJ2tOl8SzPbGZiZcjxSB5pqFBGRTIj2ZdwVuI2wMfb5wHDg4jTjygIz6wlMAEYTBk3OJhTbS4FR4iUiIllxP6Geqy0wHfgY2Bb4Efh7inGlysyOBf4EPAZcCawN7AnsTBH3S6FS4iUiIlmxobv3M7MS4FNgFLCju89MN6zUDQN6ufuMigfMrD3wOEq8Co4SLxERyYqWZrY9YbHdGYQFd7cwM4p8l4NmgEUJaWUt0ghG6keJl4iIZMXbhNGditvHRbeLfZeDt1jRL5W9k3Ac0gC0gKqIiIhIQrSchIiIiEhClHiJiIiIJESJl4iIiEhClHiJiIiIJESJl4iIiEhC/h/9gFu0zfIwagAAAABJRU5ErkJggg==\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 绘制各个模型的准确率柱状图，并在柱状图上标注值\n", + "plt.figure(figsize=(10,5))\n", + "plt.title('各个模型的准确率')\n", + "sns.barplot(x=df_result.index,y=df_result['accuracy_score'])\n", + "for i in range(df_result.shape[0]):\n", + " plt.text(i,df_result['accuracy_score'][i],round(df_result['accuracy_score'][i],3),ha='center')\n", + "# 设置字体选择\n", + "plt.xticks(rotation=90)\n", + "plt.show()\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file