[분류] 유전체 정보 품종 분류 AI 경진대회 (1) - 간단한 EDA

 

유전체 정보 품종 분류 AI 경진대회

https://dacon.io/competitions/official/236035/data

 

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import random

 

In [2]:

pd.read_csv('/content/snp_info.csv')

 

Out[2]:

	SNP_id	name	chrom	cm	pos
0	SNP_01	BTA-19852-no-rs	2	67.05460	42986890
1	SNP_02	ARS-USMARC-Parent-DQ647190-rs29013632	6	31.15670	13897068
2	SNP_03	ARS-BFGL-NGS-117009	6	68.28920	44649549
3	SNP_04	ARS-BFGL-NGS-60567	6	77.87490	53826064
4	SNP_05	BovineHD0600017032	6	80.50150	61779512
5	SNP_06	BovineHD0600017424	6	80.59540	63048481
6	SNP_07	Hapmap49442-BTA-111073	6	80.78000	64037334
7	SNP_08	BovineHD0600018638	6	82.68560	67510588
8	SNP_09	ARS-BFGL-NGS-37727	6	86.87400	73092782
9	SNP_10	BTB-01558306	7	62.06920	40827112
10	SNP_11	ARS-BFGL-NGS-44247	8	97.17310	92485682
11	SNP_12	Hapmap32827-BTA-146530	9	62.74630	55007839
12	SNP_13	BTB-00395482	9	63.41810	59692848
13	SNP_14	Hapmap40256-BTA-84189	9	66.81970	72822507
14	SNP_15	BovineHD1000000224	10	1.78774	814291

 

In [3]:

train = pd.read_csv('/content/train.csv')
test = pd.read_csv('/content/test.csv')

train.head()

 

Out[3]:

	id	father	mother	gender	trait	SNP_01	SNP_02	SNP_03	SNP_04	SNP_05	...	SNP_07	SNP_08	SNP_09	SNP_10	SNP_11	SNP_12	SNP_13	SNP_14	SNP_15	class
0	TRAIN_000	0	0	0	2	G G	A G	A A	G A	C A	...	A A	G G	A A	G G	A G	A A	A A	A A	A A	B
1	TRAIN_001	0	0	0	2	A G	A G	C A	A A	A A	...	A A	G A	A A	A G	A A	G A	G G	A A	A A	C
2	TRAIN_002	0	0	0	2	G G	G G	A A	G A	C C	...	A A	G A	G A	A G	A A	A A	A A	A A	A A	B
3	TRAIN_003	0	0	0	1	A A	G G	A A	G A	A A	...	G G	A A	G G	A G	G G	G G	G G	A A	G G	A
4	TRAIN_004	0	0	0	2	G G	G G	C C	A A	C C	...	A A	A A	A A	G G	A A	A A	A G	A A	G A	C

5 rows × 21 columns

 

In [4]:

test.head()

 

Out[4]:

	id	father	mother	gender	trait	SNP_01	SNP_02	SNP_03	SNP_04	SNP_05	SNP_06	SNP_07	SNP_08	SNP_09	SNP_10	SNP_11	SNP_12	SNP_13	SNP_14	SNP_15
0	TEST_000	0	0	0	1	A G	G G	A A	G A	A A	A G	G G	G A	G A	A G	A G	G A	G G	C A	G A
1	TEST_001	0	0	0	2	G G	A G	C C	G G	C C	A A	A A	A A	A A	G G	A G	A A	A A	A A	A A
2	TEST_002	0	0	0	2	G G	A G	A A	A A	C A	A G	A A	A A	A A	A G	A A	G A	G G	A A	G G
3	TEST_003	0	0	0	2	G G	A G	C A	A A	C C	A A	A A	A A	A A	G G	A A	G A	A G	A A	A A
4	TEST_004	0	0	0	1	A A	G G	A A	G G	A A	G G	G G	A A	G G	A G	G G	G A	G G	A A	G G

 

In [5]:

train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 262 entries, 0 to 261
Data columns (total 21 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   id      262 non-null    object
 1   father  262 non-null    int64 
 2   mother  262 non-null    int64 
 3   gender  262 non-null    int64 
 4   trait   262 non-null    int64 
 5   SNP_01  262 non-null    object
 6   SNP_02  262 non-null    object
 7   SNP_03  262 non-null    object
 8   SNP_04  262 non-null    object
 9   SNP_05  262 non-null    object
 10  SNP_06  262 non-null    object
 11  SNP_07  262 non-null    object
 12  SNP_08  262 non-null    object
 13  SNP_09  262 non-null    object
 14  SNP_10  262 non-null    object
 15  SNP_11  262 non-null    object
 16  SNP_12  262 non-null    object
 17  SNP_13  262 non-null    object
 18  SNP_14  262 non-null    object
 19  SNP_15  262 non-null    object
 20  class   262 non-null    object
dtypes: int64(4), object(17)
memory usage: 43.1+ KB

 

In [6]:

train.isna().sum()

 

Out[6]:

id        0
father    0
mother    0
gender    0
trait     0
SNP_01    0
SNP_02    0
SNP_03    0
SNP_04    0
SNP_05    0
SNP_06    0
SNP_07    0
SNP_08    0
SNP_09    0
SNP_10    0
SNP_11    0
SNP_12    0
SNP_13    0
SNP_14    0
SNP_15    0
class     0
dtype: int64

 

In [7]:

test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 175 entries, 0 to 174
Data columns (total 20 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   id      175 non-null    object
 1   father  175 non-null    int64 
 2   mother  175 non-null    int64 
 3   gender  175 non-null    int64 
 4   trait   175 non-null    int64 
 5   SNP_01  175 non-null    object
 6   SNP_02  175 non-null    object
 7   SNP_03  175 non-null    object
 8   SNP_04  175 non-null    object
 9   SNP_05  175 non-null    object
 10  SNP_06  175 non-null    object
 11  SNP_07  175 non-null    object
 12  SNP_08  175 non-null    object
 13  SNP_09  175 non-null    object
 14  SNP_10  175 non-null    object
 15  SNP_11  175 non-null    object
 16  SNP_12  175 non-null    object
 17  SNP_13  175 non-null    object
 18  SNP_14  175 non-null    object
 19  SNP_15  175 non-null    object
dtypes: int64(4), object(16)
memory usage: 27.5+ KB

 

In [8]:

test.isna().sum()

 

Out[8]:

id        0
father    0
mother    0
gender    0
trait     0
SNP_01    0
SNP_02    0
SNP_03    0
SNP_04    0
SNP_05    0
SNP_06    0
SNP_07    0
SNP_08    0
SNP_09    0
SNP_10    0
SNP_11    0
SNP_12    0
SNP_13    0
SNP_14    0
SNP_15    0
dtype: int64

 

In [9]:

train.nunique()

 

Out[9]:

id        262
father      1
mother      1
gender      1
trait       2
SNP_01      3
SNP_02      3
SNP_03      3
SNP_04      3
SNP_05      3
SNP_06      3
SNP_07      3
SNP_08      3
SNP_09      3
SNP_10      3
SNP_11      3
SNP_12      3
SNP_13      3
SNP_14      3
SNP_15      3
class       3
dtype: int64

 

In [10]:

test.nunique()

 

Out[10]:

id        175
father      1
mother      1
gender      1
trait       2
SNP_01      3
SNP_02      3
SNP_03      3
SNP_04      3
SNP_05      3
SNP_06      3
SNP_07      3
SNP_08      3
SNP_09      3
SNP_10      3
SNP_11      3
SNP_12      3
SNP_13      3
SNP_14      3
SNP_15      3
dtype: int64

father, mother, gender는 유일값이 1개인 필요없는 칼럼

 

In [11]:

train['trait'].value_counts()

 

Out[11]:

2    193
1     69
Name: trait, dtype: int64

개체 표현형 정보는 종류가 2가지, '2'가 다수

 

In [12]:

pd.pivot_table(train, index=['trait', 'class'],
               values='id',
               aggfunc='count')

 

Out[12]:

		id
trait	class
1	A	69
2	B	114
	C	79

 

개체 표현형 정보가 '1'인 경우는 무조건 A품종

'2'일 때, 중 품종이 B인 경우가 더 많다

 

In [31]:

sns.countplot(train, x='trait', hue='class', palette='Set2')

 

Out[31]:

<Axes: xlabel='trait', ylabel='count'>

 

 

In [15]:

train = train.iloc[:, 4:]
test = test.iloc[:, 4:]

 

In [16]:

train.head(3)

 

Out[16]:

	trait	SNP_01	SNP_02	SNP_03	SNP_04	SNP_05	SNP_06	SNP_07	SNP_08	SNP_09	SNP_10	SNP_11	SNP_12	SNP_13	SNP_14	SNP_15	class
0	2	G G	A G	A A	G A	C A	A A	A A	G G	A A	G G	A G	A A	A A	A A	A A	B
1	2	A G	A G	C A	A A	A A	A G	A A	G A	A A	A G	A A	G A	G G	A A	A A	C
2	2	G G	G G	A A	G A	C C	G G	A A	G A	G A	A G	A A	A A	A A	A A	A A	B

 

In [20]:

snp_col = [f'SNP_{str(x).zfill(2)}' for x in range(1, 16)]
snp_col

 

Out[20]:

['SNP_01',
 'SNP_02',
 'SNP_03',
 'SNP_04',
 'SNP_05',
 'SNP_06',
 'SNP_07',
 'SNP_08',
 'SNP_09',
 'SNP_10',
 'SNP_11',
 'SNP_12',
 'SNP_13',
 'SNP_14',
 'SNP_15']

 

 

In [30]:

for i in snp_col:
  train.groupby(i)['class'].value_counts().unstack().plot.bar(figsize=(6,4))