Data Science/Dacon
[회귀] 감귤 착과량 예측 AI 경진대회
얆생
2023. 6. 14. 14:49
감귤 착과량 예측 AI 경진대회
https://dacon.io/competitions/official/236038/overview/description
- ID : 과수나무 고유 ID
- 착과량(int) : 실제 감귤 착과량 (Target)
- 나무 생육 상태 Features (5개): 수고(m), 수관폭1(min), 수관폭2(max), 수관폭평균(수관폭1과 수관폭2의 평균) (데이터 기입은 cm 단위)
- 새순 Features (89개): 2022년 09월 01일 ~ 2022년 11월 28일에 일별 측정된 새순 데이터
- 엽록소 Features (89개): 2022년 09월 01일 ~ 2022년 11월 28일에 일별 측정된 엽록소 데이터
- 목적: 감귤나무의 나무 생육 상태, 엽록소 및 새순 정보로부터 감귤 착과량을 회귀 예측
- 시간이 경과할 수록 새순 피처의 변화, 엽록소 피처의 변화를 따로 봐야할 것 같음
- 감귤 착과량에 가장 영향을 많이 주는 피처를 찾기/나무 생육 상태 5가지에 따른 착과량 비교하기
In [1]:
!sudo apt-get install -y fonts-nanum
!sudo fc-cache -fv
!rm ~/.cache/matplotlib -rf
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following NEW packages will be installed:
fonts-nanum
0 upgraded, 1 newly installed, 0 to remove and 46 not upgraded.
Need to get 9,599 kB of archives.
After this operation, 29.6 MB of additional disk space will be used.
Get:1 http://archive.ubuntu.com/ubuntu focal/universe amd64 fonts-nanum all 20180306-3 [9,599 kB]
Fetched 9,599 kB in 0s (54.6 MB/s)
debconf: unable to initialize frontend: Dialog
debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)
debconf: falling back to frontend: Readline
debconf: unable to initialize frontend: Readline
debconf: (This frontend requires a controlling tty.)
debconf: falling back to frontend: Teletype
dpkg-preconfigure: unable to re-open stdin:
Selecting previously unselected package fonts-nanum.
(Reading database ... 122541 files and directories currently installed.)
Preparing to unpack .../fonts-nanum_20180306-3_all.deb ...
Unpacking fonts-nanum (20180306-3) ...
Setting up fonts-nanum (20180306-3) ...
Processing triggers for fontconfig (2.13.1-2ubuntu3) ...
/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs
/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 3 dirs
/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs
/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs
/usr/share/fonts/truetype/nanum: caching, new cache contents: 10 fonts, 0 dirs
/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs
/root/.local/share/fonts: skipping, no such directory
/root/.fonts: skipping, no such directory
/usr/share/fonts/truetype: skipping, looped directory detected
/usr/share/fonts/truetype/humor-sans: skipping, looped directory detected
/usr/share/fonts/truetype/liberation: skipping, looped directory detected
/usr/share/fonts/truetype/nanum: skipping, looped directory detected
/var/cache/fontconfig: cleaning cache directory
/root/.cache/fontconfig: not cleaning non-existent cache directory
/root/.fontconfig: not cleaning non-existent cache directory
fc-cache: succeeded
In [11]:
import pandas as pd
import seaborn as sns
import numpy as np
import sklearn
import matplotlib.pyplot as plt
plt.rc('font', family='NanumBarunGothic')
간단 EDA
In [13]:
train = pd.read_csv('/content/train.csv')
test = pd.read_csv('/content/test.csv')
In [14]:
train.head()
Out[14]:
| ID | 착과량(int) | 수고(m) | 수관폭1(min) | 수관폭2(max) | 수관폭평균 | 2022-09-01 새순 | 2022-09-02 새순 | 2022-09-03 새순 | 2022-09-04 새순 | ... | 2022-11-19 엽록소 | 2022-11-20 엽록소 | 2022-11-21 엽록소 | 2022-11-22 엽록소 | 2022-11-23 엽록소 | 2022-11-24 엽록소 | 2022-11-25 엽록소 | 2022-11-26 엽록소 | 2022-11-27 엽록소 | 2022-11-28 엽록소 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TRAIN_0000 | 692 | 275.0 | 287.0 | 292.0 | 289.5 | 2.8 | 2.8 | 2.7 | 2.7 | ... | 70.978249 | 70.876794 | 70.705253 | 70.559603 | 70.427356 | 70.340491 | 70.293830 | 70.262422 | 70.169841 | 70.043251 |
| 1 | TRAIN_0001 | 534 | 293.0 | 284.0 | 336.0 | 310.0 | 3.3 | 3.3 | 3.3 | 3.2 | ... | 71.535483 | 71.382303 | 71.253604 | 71.092665 | 70.955608 | 70.796630 | 70.597550 | 70.565088 | 70.560502 | 70.427600 |
| 2 | TRAIN_0002 | 634 | 300.0 | 392.0 | 450.0 | 421.0 | 3.0 | 2.9 | 2.9 | 2.9 | ... | 71.279804 | 71.199570 | 71.144020 | 71.026740 | 70.920038 | 70.876723 | 70.710129 | 70.595971 | 70.418203 | 70.399578 |
| 3 | TRAIN_0003 | 639 | 289.0 | 368.0 | 379.0 | 373.5 | 3.1 | 3.0 | 3.0 | 3.0 | ... | 69.934615 | 69.884124 | 69.845683 | 69.794682 | 69.779813 | 69.614644 | 69.455404 | 69.281590 | 69.238689 | 69.133970 |
| 4 | TRAIN_0004 | 496 | 306.0 | 353.0 | 358.0 | 355.5 | 3.7 | 3.6 | 3.6 | 3.6 | ... | 68.313016 | 68.285364 | 68.209860 | 68.209458 | 68.040083 | 67.859963 | 67.775556 | 67.701456 | 67.504244 | 67.410093 |
5 rows × 184 columns
In [15]:
test.head()
Out[15]:
| ID | 수고(m) | 수관폭1(min) | 수관폭2(max) | 수관폭평균 | 2022-09-01 새순 | 2022-09-02 새순 | 2022-09-03 새순 | 2022-09-04 새순 | 2022-09-05 새순 | ... | 2022-11-19 엽록소 | 2022-11-20 엽록소 | 2022-11-21 엽록소 | 2022-11-22 엽록소 | 2022-11-23 엽록소 | 2022-11-24 엽록소 | 2022-11-25 엽록소 | 2022-11-26 엽록소 | 2022-11-27 엽록소 | 2022-11-28 엽록소 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | TEST_0000 | 231.0 | 219.0 | 302.0 | 260.5 | 4.3 | 4.2 | 4.2 | 4.1 | 4.1 | ... | 72.091497 | 71.906176 | 71.886799 | 71.858720 | 71.725540 | 71.531618 | 71.476935 | 71.321237 | 71.184295 | 71.000223 |
| 1 | TEST_0001 | 206.0 | 324.0 | 346.0 | 335.0 | 2.5 | 2.5 | 2.5 | 2.5 | 2.5 | ... | 68.423776 | 68.353200 | 68.346777 | 68.188159 | 67.995233 | 67.879958 | 67.744820 | 67.673522 | 67.539973 | 67.447849 |
| 2 | TEST_0002 | 282.0 | 373.0 | 404.0 | 388.5 | 4.7 | 4.7 | 4.6 | 4.6 | 4.5 | ... | 74.435783 | 74.247468 | 74.142664 | 74.070401 | 73.892408 | 73.796143 | 73.615838 | 73.440136 | 73.387051 | 73.228319 |
| 3 | TEST_0003 | 234.0 | 337.0 | 357.0 | 347.0 | 3.6 | 3.6 | 3.6 | 3.6 | 3.5 | ... | 73.570735 | 73.420243 | 73.313538 | 73.149298 | 73.109599 | 72.924511 | 72.847411 | 72.838263 | 72.672156 | 72.538171 |
| 4 | TEST_0004 | 222.0 | 329.0 | 347.0 | 338.0 | 2.7 | 2.7 | 2.7 | 2.7 | 2.6 | ... | 72.216758 | 72.163153 | 72.052952 | 71.905982 | 71.766141 | 71.746813 | 71.668847 | 71.501043 | 71.419715 | 71.328224 |
5 rows × 183 columns
In [16]:
train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2207 entries, 0 to 2206
Columns: 184 entries, ID to 2022-11-28 엽록소
dtypes: float64(182), int64(1), object(1)
memory usage: 3.1+ MB
In [17]:
train.isna().sum()
Out[17]:
ID 0
착과량(int) 0
수고(m) 0
수관폭1(min) 0
수관폭2(max) 0
..
2022-11-24 엽록소 0
2022-11-25 엽록소 0
2022-11-26 엽록소 0
2022-11-27 엽록소 0
2022-11-28 엽록소 0
Length: 184, dtype: int64In [18]:
test.isna().sum()
Out[18]:
ID 0
수고(m) 0
수관폭1(min) 0
수관폭2(max) 0
수관폭평균 0
..
2022-11-24 엽록소 0
2022-11-25 엽록소 0
2022-11-26 엽록소 0
2022-11-27 엽록소 0
2022-11-28 엽록소 0
Length: 183, dtype: int64In [19]:
train.describe()
Out[19]:
| 착과량(int) | 수고(m) | 수관폭1(min) | 수관폭2(max) | 수관폭평균 | 2022-09-01 새순 | 2022-09-02 새순 | 2022-09-03 새순 | 2022-09-04 새순 | 2022-09-05 새순 | ... | 2022-11-19 엽록소 | 2022-11-20 엽록소 | 2022-11-21 엽록소 | 2022-11-22 엽록소 | 2022-11-23 엽록소 | 2022-11-24 엽록소 | 2022-11-25 엽록소 | 2022-11-26 엽록소 | 2022-11-27 엽록소 | 2022-11-28 엽록소 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2207.000000 | 2207.00000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | ... | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 | 2207.000000 |
| mean | 406.222474 | 243.72995 | 315.186226 | 358.824196 | 337.005211 | 3.774807 | 3.736928 | 3.701495 | 3.663435 | 3.627503 | ... | 68.992036 | 68.890661 | 68.791536 | 68.692934 | 68.593709 | 68.493569 | 68.393050 | 68.291350 | 68.192009 | 68.093096 |
| std | 218.978302 | 35.88914 | 66.327994 | 68.862898 | 64.859684 | 0.774181 | 0.764067 | 0.754751 | 0.745394 | 0.733896 | ... | 4.124586 | 4.124429 | 4.125817 | 4.128586 | 4.130005 | 4.130072 | 4.131859 | 4.134976 | 4.135591 | 4.136995 |
| min | 1.000000 | 95.00000 | 95.000000 | 123.000000 | 109.000000 | 2.300000 | 2.200000 | 2.200000 | 2.200000 | 2.200000 | ... | 54.246243 | 54.154888 | 54.143798 | 54.028089 | 53.884892 | 53.695860 | 53.649032 | 53.644994 | 53.450069 | 53.379849 |
| 25% | 221.000000 | 223.00000 | 271.500000 | 313.500000 | 294.000000 | 3.100000 | 3.100000 | 3.050000 | 3.000000 | 3.000000 | ... | 66.195524 | 66.075372 | 65.995234 | 65.906703 | 65.765257 | 65.691065 | 65.609606 | 65.487863 | 65.374528 | 65.278624 |
| 50% | 404.000000 | 243.00000 | 316.000000 | 364.000000 | 340.500000 | 3.800000 | 3.700000 | 3.700000 | 3.700000 | 3.600000 | ... | 69.048956 | 68.920295 | 68.841060 | 68.731164 | 68.647716 | 68.514740 | 68.392436 | 68.292889 | 68.207648 | 68.102235 |
| 75% | 593.500000 | 267.00000 | 361.000000 | 402.000000 | 380.000000 | 4.400000 | 4.400000 | 4.300000 | 4.300000 | 4.300000 | ... | 71.740281 | 71.615746 | 71.504264 | 71.390195 | 71.311027 | 71.227033 | 71.152030 | 71.038257 | 70.947707 | 70.850934 |
| max | 799.000000 | 381.00000 | 559.000000 | 584.000000 | 571.500000 | 5.300000 | 5.300000 | 5.200000 | 5.200000 | 5.100000 | ... | 83.363883 | 83.227379 | 83.036103 | 82.978876 | 82.842139 | 82.722266 | 82.575149 | 82.491041 | 82.371832 | 82.321938 |
8 rows × 183 columns
In [20]:
growth = train[['착과량(int)', '수고(m)', '수관폭1(min)', '수관폭2(max)', '수관폭평균']]
growth
Out[20]:
| 착과량(int) | 수고(m) | 수관폭1(min) | 수관폭2(max) | 수관폭평균 | |
|---|---|---|---|---|---|
| 0 | 692 | 275.0 | 287.0 | 292.0 | 289.5 |
| 1 | 534 | 293.0 | 284.0 | 336.0 | 310.0 |
| 2 | 634 | 300.0 | 392.0 | 450.0 | 421.0 |
| 3 | 639 | 289.0 | 368.0 | 379.0 | 373.5 |
| 4 | 496 | 306.0 | 353.0 | 358.0 | 355.5 |
| ... | ... | ... | ... | ... | ... |
| 2202 | 119 | 230.0 | 353.0 | 367.0 | 360.0 |
| 2203 | 611 | 266.0 | 337.0 | 400.0 | 368.5 |
| 2204 | 650 | 237.0 | 353.0 | 362.0 | 357.5 |
| 2205 | 363 | 281.0 | 337.0 | 403.0 | 370.0 |
| 2206 | 17 | 228.0 | 389.0 | 398.0 | 393.5 |
2207 rows × 5 columns
In [21]:
growth.corr()
Out[21]:
| 착과량(int) | 수고(m) | 수관폭1(min) | 수관폭2(max) | 수관폭평균 | |
|---|---|---|---|---|---|
| 착과량(int) | 1.000000 | 0.007002 | 0.021279 | 0.037168 | 0.030611 |
| 수고(m) | 0.007002 | 1.000000 | 0.456394 | 0.484295 | 0.490456 |
| 수관폭1(min) | 0.021279 | 0.456394 | 1.000000 | 0.841330 | 0.957948 |
| 수관폭2(max) | 0.037168 | 0.484295 | 0.841330 | 1.000000 | 0.961049 |
| 수관폭평균 | 0.030611 | 0.490456 | 0.957948 | 0.961049 | 1.000000 |
In [22]:
sns.heatmap(growth.corr(), cmap='Greens', annot=True)
Out[22]:
<Axes: >
- 나무 생육 상태 4가지와 착과량은 상관성이 없음
In [23]:
saesun = train.loc[:, '2022-09-01 새순':'2022-11-28 새순']
saesun
Out[23]:
| 2022-09-01 새순 | 2022-09-02 새순 | 2022-09-03 새순 | 2022-09-04 새순 | 2022-09-05 새순 | 2022-09-06 새순 | 2022-09-07 새순 | 2022-09-08 새순 | 2022-09-09 새순 | 2022-09-10 새순 | ... | 2022-11-19 새순 | 2022-11-20 새순 | 2022-11-21 새순 | 2022-11-22 새순 | 2022-11-23 새순 | 2022-11-24 새순 | 2022-11-25 새순 | 2022-11-26 새순 | 2022-11-27 새순 | 2022-11-28 새순 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.8 | 2.8 | 2.7 | 2.7 | 2.7 | 2.7 | 2.6 | 2.6 | 2.6 | 2.6 | ... | 0.9 | 0.8 | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.6 |
| 1 | 3.3 | 3.3 | 3.3 | 3.2 | 3.2 | 3.1 | 3.1 | 3.1 | 3.0 | 3.0 | ... | 0.7 | 0.7 | 0.6 | 0.6 | 0.6 | 0.5 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 3.0 | 2.9 | 2.9 | 2.9 | 2.9 | 2.8 | 2.8 | 2.8 | 2.8 | 2.7 | ... | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 | 0.7 | 0.6 | 0.6 | 0.6 | 0.5 |
| 3 | 3.1 | 3.0 | 3.0 | 3.0 | 3.0 | 2.9 | 2.9 | 2.9 | 2.8 | 2.8 | ... | 0.9 | 0.9 | 0.9 | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 |
| 4 | 3.7 | 3.6 | 3.6 | 3.6 | 3.5 | 3.5 | 3.5 | 3.4 | 3.4 | 3.3 | ... | 0.9 | 0.9 | 0.8 | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 | 0.6 | 0.6 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2202 | 4.7 | 4.6 | 4.5 | 4.5 | 4.4 | 4.4 | 4.3 | 4.3 | 4.2 | 4.2 | ... | 0.8 | 0.8 | 0.8 | 0.7 | 0.7 | 0.6 | 0.6 | 0.5 | 0.0 | 0.0 |
| 2203 | 3.1 | 3.0 | 3.0 | 3.0 | 2.9 | 2.9 | 2.9 | 2.9 | 2.8 | 2.8 | ... | 0.9 | 0.9 | 0.8 | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 | 0.7 | 0.6 |
| 2204 | 3.0 | 3.0 | 2.9 | 2.9 | 2.9 | 2.9 | 2.8 | 2.8 | 2.8 | 2.8 | ... | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 | 0.7 | 0.6 | 0.6 | 0.6 | 0.5 |
| 2205 | 3.9 | 3.9 | 3.8 | 3.8 | 3.8 | 3.7 | 3.7 | 3.6 | 3.6 | 3.6 | ... | 0.8 | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 | 0.6 | 0.6 | 0.5 | 0.0 |
| 2206 | 5.3 | 5.2 | 5.2 | 5.1 | 5.0 | 5.0 | 4.9 | 4.9 | 4.8 | 4.8 | ... | 0.9 | 0.8 | 0.8 | 0.7 | 0.7 | 0.6 | 0.6 | 0.5 | 0.0 | 0.0 |
2207 rows × 89 columns
In [24]:
saesun.mean()
Out[24]:
2022-09-01 새순 3.774807
2022-09-02 새순 3.736928
2022-09-03 새순 3.701495
2022-09-04 새순 3.663435
2022-09-05 새순 3.627503
...
2022-11-24 새순 0.682102
2022-11-25 새순 0.632170
2022-11-26 새순 0.559991
2022-11-27 새순 0.454599
2022-11-28 새순 0.352832
Length: 89, dtype: float64- 시간이 지날수록 새순 개수가 줄어듬
In [25]:
yeoprokso = train.loc[:, '2022-09-01 엽록소':'2022-11-28 엽록소']
yeoprokso
Out[25]:
| 2022-09-01 엽록소 | 2022-09-02 엽록소 | 2022-09-03 엽록소 | 2022-09-04 엽록소 | 2022-09-05 엽록소 | 2022-09-06 엽록소 | 2022-09-07 엽록소 | 2022-09-08 엽록소 | 2022-09-09 엽록소 | 2022-09-10 엽록소 | ... | 2022-11-19 엽록소 | 2022-11-20 엽록소 | 2022-11-21 엽록소 | 2022-11-22 엽록소 | 2022-11-23 엽록소 | 2022-11-24 엽록소 | 2022-11-25 엽록소 | 2022-11-26 엽록소 | 2022-11-27 엽록소 | 2022-11-28 엽록소 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 78.336504 | 78.243462 | 78.166501 | 78.127526 | 78.058021 | 77.915689 | 77.861660 | 77.746645 | 77.592138 | 77.572477 | ... | 70.978249 | 70.876794 | 70.705253 | 70.559603 | 70.427356 | 70.340491 | 70.293830 | 70.262422 | 70.169841 | 70.043251 |
| 1 | 80.116691 | 79.974397 | 79.890399 | 79.709278 | 79.531061 | 79.348139 | 79.151023 | 79.128739 | 79.084197 | 78.924476 | ... | 71.535483 | 71.382303 | 71.253604 | 71.092665 | 70.955608 | 70.796630 | 70.597550 | 70.565088 | 70.560502 | 70.427600 |
| 2 | 79.118529 | 78.941452 | 78.928197 | 78.741878 | 78.713691 | 78.654247 | 78.461743 | 78.323264 | 78.219696 | 78.123820 | ... | 71.279804 | 71.199570 | 71.144020 | 71.026740 | 70.920038 | 70.876723 | 70.710129 | 70.595971 | 70.418203 | 70.399578 |
| 3 | 77.936262 | 77.839523 | 77.815607 | 77.644644 | 77.546555 | 77.480441 | 77.383665 | 77.282036 | 77.126354 | 77.121300 | ... | 69.934615 | 69.884124 | 69.845683 | 69.794682 | 69.779813 | 69.614644 | 69.455404 | 69.281590 | 69.238689 | 69.133970 |
| 4 | 76.233231 | 76.186572 | 76.031032 | 75.886553 | 75.736449 | 75.735036 | 75.642223 | 75.458496 | 75.380945 | 75.272054 | ... | 68.313016 | 68.285364 | 68.209860 | 68.209458 | 68.040083 | 67.859963 | 67.775556 | 67.701456 | 67.504244 | 67.410093 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2202 | 74.024614 | 73.876933 | 73.853205 | 73.721799 | 73.666974 | 73.540144 | 73.449933 | 73.349051 | 73.310852 | 73.273704 | ... | 66.347384 | 66.293609 | 66.193131 | 66.176590 | 66.109845 | 66.008775 | 65.889675 | 65.734066 | 65.586656 | 65.560547 |
| 2203 | 81.920876 | 81.898801 | 81.769536 | 81.607251 | 81.456723 | 81.408951 | 81.312932 | 81.181893 | 81.027196 | 81.010124 | ... | 73.438844 | 73.432194 | 73.355195 | 73.329491 | 73.271361 | 73.088750 | 73.071705 | 73.069036 | 72.952559 | 72.944396 |
| 2204 | 75.486518 | 75.451905 | 75.315223 | 75.252547 | 75.186170 | 75.099348 | 75.093451 | 75.050447 | 74.877088 | 74.711044 | ... | 67.576066 | 67.404347 | 67.394009 | 67.247428 | 67.117025 | 66.933771 | 66.734617 | 66.572095 | 66.506006 | 66.479459 |
| 2205 | 74.110017 | 74.017373 | 73.886675 | 73.720486 | 73.712709 | 73.532512 | 73.352104 | 73.230786 | 73.183439 | 73.006241 | ... | 66.766379 | 66.602606 | 66.510781 | 66.334637 | 66.323613 | 66.248731 | 66.202532 | 66.108962 | 65.934979 | 65.812084 |
| 2206 | 80.830756 | 80.687949 | 80.517089 | 80.339555 | 80.157257 | 79.970405 | 79.781988 | 79.764210 | 79.724992 | 79.591028 | ... | 72.885385 | 72.734930 | 72.629823 | 72.454654 | 72.389570 | 72.271680 | 72.208433 | 72.070977 | 71.872453 | 71.870512 |
2207 rows × 89 columns
In [26]:
yeoprokso.mean()
Out[26]:
2022-09-01 엽록소 76.899788
2022-09-02 엽록소 76.801492
2022-09-03 엽록소 76.699545
2022-09-04 엽록소 76.599512
2022-09-05 엽록소 76.500475
...
2022-11-24 엽록소 68.493569
2022-11-25 엽록소 68.393050
2022-11-26 엽록소 68.291350
2022-11-27 엽록소 68.192009
2022-11-28 엽록소 68.093096
Length: 89, dtype: float64- 시간이 지날수록 엽록소도 줄어들지만, 급격한 변화는 아님
전처리
- NMAE(Normalized Mean Absolute Error): 시계열 데이터일 때 많이 이용하는 평가산식
- MAE(Mean Absolute Error): 예측값과 실제값간의 차이에 절댓값을 취하고 이를 평균 낸 지표. 값이 작을수록 잘 예측한 것
- 정규화된 MAE이기 때문에 편향되지 않아 scale이 다른 데이터셋의 MAE 비교에 용이함
NMAE 평가를 위한 코드
In [27]:
def NMAE(true, pred):
mae = np.mean(np.abs(true-pred))
score = mae / np.mean(np.abs(true))
return score
파생변수 생성
period_i_새순 : 9일의 새순 평균 컬럼
새순 diff : 새순 max 컬럼 - 새순 min 컬럼
새순 max 컬럼
새순 min 컬럼
나무 부피 컬럼 : 수고(m) x 수관폭평균
엽록소 컬럼의 경우 target 값과의 연관성이 부족하다 생각해 all drop
In [28]:
def reduce_col(df, trainortest):
saesun = train.loc[:, '2022-09-01 새순':'2022-11-28 새순']
yeoprokso = train.loc[:, '2022-09-01 엽록소':'2022-11-28 엽록소']
sarr = np.array(saesun.columns)
sarr = np.append(sarr, 'd').reshape(10, 9) #새순데이터가 89개이므로 1개를 임의로 추가해서 90개 맞춰준 후 reshape하기
sdate_dic = {'period_1 새순':sarr[0],
'period_2 새순':sarr[1],
'period_3 새순':sarr[2],
'period_4 새순':sarr[3],
'period_5 새순':sarr[4],
'period_6 새순':sarr[5],
'period_7 새순':sarr[6],
'period_8 새순':sarr[7],
'period_9 새순':sarr[8],
'period_10 새순':sarr[9][:-1]
}
new = pd.DataFrame()
for period, dates in sdate_dic.items():
new[period] = saesun[dates].mean(axis=1)
new['새순 diff'] = train['2022-09-01 새순']-df['2022-11-28 새순']
new['새순 max'] = train['2022-09-01 새순']
new['새순 min'] = train['2022-11-28 새순']
if trainortest == 'train':
new['착과량'] = train['착과량(int)']
new['나무부피'] = train['수고(m)'] * train['수관폭평균']
return new
데이터셋의 앞서 생성한 함수 적용하고 X, y에 간추린 피처들만 할당
In [29]:
X = reduce_col(train, 'train').drop('착과량', axis=1)
y = reduce_col(train, 'train')['착과량']
In [30]:
X.columns
Out[30]:
Index(['period_1 새순', 'period_2 새순', 'period_3 새순', 'period_4 새순',
'period_5 새순', 'period_6 새순', 'period_7 새순', 'period_8 새순',
'period_9 새순', 'period_10 새순', '새순 diff', '새순 max', '새순 min', '나무부피'],
dtype='object')모델링(1) RandomForestRegressor
그리드서치
In [32]:
from sklearn.model_selection import ShuffleSplit
#임의분할 교차검정, 사전에 설정한 비율에 따라 데이터셋 인덱스를 무작위로 분할
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score, cross_validate
import time
start = time.time()
rf = RandomForestRegressor(n_jobs = -1, random_state=0)
param_grid = {'max_depth':[3, 4, 5, 6, 7,],
'bootstrap': [True, False],
'max_features': ['auto', 'sqrt', 'log2'],
'criterion': ['squared_error', 'absolute_error', 'friedman_mse', 'poisson']}
ss = ShuffleSplit(test_size=0.3, random_state=0)
grid_rf_cv = GridSearchCV(rf, return_train_score=True,
param_grid=param_grid,
cv=ss,
verbose=0, scoring='neg_mean_absolute_error',
n_jobs=-1)
grid_rf_cv.fit(X, y)
end = time.time()
print('수행 시간: {0:.3f}'.format(end-start))
print('최적 매개변수 조합:', grid_rf_cv.best_params_)
print('최고의 교차 검증 점수:', grid_rf_cv.best_score_)
수행 시간: 1542.643
최적 매개변수 조합: {'bootstrap': False, 'criterion': 'friedman_mse', 'max_depth': 6, 'max_features': 'sqrt'}
최고의 교차 검증 점수: -30.18017600138905
최적 모델을 best_rf에 저장하고 NMAE 확인
In [33]:
best_rf = grid_rf_cv.best_estimator_
y_pred_rf = best_rf.predict(X)
print(f'NMAE: {NMAE(y, y_pred_rf)}')
NMAE: 0.06610512938333356
test data로 예측하기
In [34]:
X_test = reduce_col(test, 'test')
test_pred_rf = best_rf.predict(X_test)
In [35]:
test_pred_rf
Out[35]:
array([645.19767757, 551.3456464 , 639.02808225, ..., 639.0451265 ,
360.25043723, 24.2362344 ])모델링(2) XGBoostRegressor
In [36]:
import xgboost as xgb
그리드서치
In [37]:
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import GridSearchCV
xgbr = xgb.XGBRegressor()
param_grid = {'learning_rate': [0.001, 0.01, 0.1, 0.2],
'max_depth': [3 ,4 ,5, 6, 7]}
ss = ShuffleSplit(test_size=0.3, random_state=0)
grid_xgb_cv = GridSearchCV(xgbr, return_train_score=True,
param_grid=param_grid,
cv=ss,
verbose=0, scoring='neg_mean_absolute_error',
n_jobs=-1)
grid_xgb_cv.fit(X, y)
print('최적 매개변수 조합:', grid_xgb_cv.best_params_)
print('최고의 교차 검증 점수:', grid_xgb_cv.best_score_)
최적 매개변수 조합: {'learning_rate': 0.1, 'max_depth': 3}
최고의 교차 검증 점수: -30.630914427899672
최적 모델을 best_xgb에 저장하고 NMAE 확인
In [38]:
best_xgb = grid_xgb_cv.best_estimator_
y_pred_xgb = best_xgb.predict(X)
print(f'NMAE: {NMAE(y, y_pred_xgb)}')
NMAE: 0.06664120790058173
test data로 예측하기
In [40]:
test_pred_xgb = best_xgb.predict(X_test)
모델 앙상블
In [41]:
final_pred = 0.5 * test_pred_rf + 0.5 * test_pred_xgb
In [48]:
final_pred
Out[48]:
array([644.03145597, 548.1806357 , 636.13910216, ..., 631.92289284,
360.16364025, 26.20572459])교차 검증 및 변수 중요도 확인
랜덤포레스트 교차검증
In [49]:
from sklearn.model_selection import cross_validate
cv_scores = cross_validate(rf, X, y,
cv=5, return_train_score=True,
n_jobs=-1)
cv_scores_2 = cross_validate(best_rf, X, y,
cv=5, return_train_score=True,
n_jobs=-1)
print('첫 번째 교차 검증 결과')
print('훈련 세트 점수: {0:.3f}'.format(np.mean(cv_scores['train_score'])))
print('검증 세트 점수: {0:.3f}'.format(np.mean(cv_scores['test_score'])))
print('\n')
print('두 번째 교차 검증 결과')
print('훈련 세트 점수: {0:.3f}'.format(np.mean(cv_scores_2['train_score'])))
print('검증 세트 점수: {0:.3f}'.format(np.mean(cv_scores_2['test_score'])))
첫 번째 교차 검증 결과
훈련 세트 점수: 0.995
검증 세트 점수: 0.967
두 번째 교차 검증 결과
훈련 세트 점수: 0.979
검증 세트 점수: 0.970
XGBoost 교차검증
In [50]:
from sklearn.model_selection import cross_validate
cv_scores = cross_validate(xgbr, X, y,
cv=5, return_train_score=True,
n_jobs=-1)
cv_scores_2 = cross_validate(best_xgb, X, y,
cv=5, return_train_score=True,
n_jobs=-1)
print('첫 번째 교차 검증 결과')
print('훈련 세트 점수: {0:.3f}'.format(np.mean(cv_scores['train_score'])))
print('검증 세트 점수: {0:.3f}'.format(np.mean(cv_scores['test_score'])))
print('\n')
print('두 번째 교차 검증 결과')
print('훈련 세트 점수: {0:.3f}'.format(np.mean(cv_scores_2['train_score'])))
print('검증 세트 점수: {0:.3f}'.format(np.mean(cv_scores_2['test_score'])))
첫 번째 교차 검증 결과
훈련 세트 점수: 0.998
검증 세트 점수: 0.963
두 번째 교차 검증 결과
훈련 세트 점수: 0.978
검증 세트 점수: 0.970
랜덤포레스트 피처 중요도
In [51]:
importances = best_rf.feature_importances_
features = X.columns
new = pd.DataFrame({'피처': np.array(features), '중요도': importances})
new.sort_values(by='중요도', ascending=False)
Out[51]:
| 피처 | 중요도 | |
|---|---|---|
| 1 | period_2 새순 | 0.206323 |
| 2 | period_3 새순 | 0.203110 |
| 0 | period_1 새순 | 0.155636 |
| 11 | 새순 max | 0.147918 |
| 3 | period_4 새순 | 0.094341 |
| 4 | period_5 새순 | 0.062433 |
| 10 | 새순 diff | 0.045945 |
| 6 | period_7 새순 | 0.034997 |
| 5 | period_6 새순 | 0.031448 |
| 7 | period_8 새순 | 0.009795 |
| 12 | 새순 min | 0.006348 |
| 9 | period_10 새순 | 0.000857 |
| 8 | period_9 새순 | 0.000478 |
| 13 | 나무부피 | 0.000371 |
XGBoost 피처 중요도
In [52]:
importances = best_xgb.feature_importances_
features = X.columns
new = pd.DataFrame({'피처': np.array(features), '중요도': importances})
new.sort_values(by='중요도', ascending=False)
Out[52]:
| 피처 | 중요도 | |
|---|---|---|
| 0 | period_1 새순 | 0.558243 |
| 1 | period_2 새순 | 0.244044 |
| 11 | 새순 max | 0.082362 |
| 2 | period_3 새순 | 0.047165 |
| 10 | 새순 diff | 0.043126 |
| 3 | period_4 새순 | 0.010819 |
| 12 | 새순 min | 0.007358 |
| 4 | period_5 새순 | 0.002545 |
| 6 | period_7 새순 | 0.000885 |
| 9 | period_10 새순 | 0.000765 |
| 8 | period_9 새순 | 0.000737 |
| 13 | 나무부피 | 0.000658 |
| 5 | period_6 새순 | 0.000652 |
| 7 | period_8 새순 | 0.000642 |